{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table border=\"0\">\n",
    "    <tr>\n",
    "        <td>\n",
    "            <img src=\"https://ictd2016.files.wordpress.com/2016/04/microsoft-research-logo-copy.jpg\" style=\"width 30px;\" />\n",
    "             </td>\n",
    "        <td>\n",
    "            <img src=\"https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/MSR-ALICE-HeaderGraphic-1920x720_1-800x550.jpg\" style=\"width 100px;\"/></td>\n",
    "        </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Double Machine Learning: Use Cases and Examples\n",
    "\n",
    "Double Machine Learning (DML) is an algorithm that applies arbitrary machine learning methods\n",
    "to fit the treatment and response, then uses a linear model to predict the response residuals\n",
    "from the treatment residuals.\n",
    "\n",
    "The EconML SDK implements the following DML classes:\n",
    "* LinearDML: suitable for estimating heterogeneous treatment effects.\n",
    "* SparseLinearDML: suitable for the case when $W$ is high dimensional vector and both the first stage and second stage estimate are linear.\n",
    "\n",
    "In ths notebook, we show the performance of the DML on both synthetic data and observational data.\n",
    "\n",
    "**Notebook contents:**\n",
    "\n",
    "1. Example usage with single continuous treatment synthetic data\n",
    "2. Example usage with single binary treatment synthetic data\n",
    "3. Example usage with multiple continuous treatment synthetic data\n",
    "4. Example usage with single continuous treatment observational data\n",
    "5. Example usage with multiple continuous treatment, multiple outcome observational data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Main imports\n",
    "from econml.dml import DML, LinearDML, SparseLinearDML, CausalForestDML\n",
    "\n",
    "# Helper imports\n",
    "import numpy as np\n",
    "from itertools import product\n",
    "from sklearn.linear_model import (Lasso, MultiTaskElasticNetCV)\n",
    "from sklearn.ensemble import RandomForestRegressor,RandomForestClassifier\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Example Usage with Single Continuous Treatment Synthetic Data and Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. DGP \n",
    "We use the data generating process (DGP) from [here](https://arxiv.org/abs/1806.03467). The DGP is described by the following equations:\n",
    "\n",
    "\\begin{align}\n",
    "T =& \\langle W, \\beta\\rangle + \\eta, & \\;\\eta \\sim \\text{Uniform}(-1, 1)\\\\\n",
    "Y =& T\\cdot \\theta(X) + \\langle W, \\gamma\\rangle + \\epsilon, &\\; \\epsilon \\sim \\text{Uniform}(-1, 1)\\\\\n",
    "W \\sim& \\text{Normal}(0,\\, I_{n_w})\\\\\n",
    "X \\sim& \\text{Uniform}(0,1)^{n_x}\n",
    "\\end{align}\n",
    "\n",
    "where $W$ is a matrix of high-dimensional confounders and $\\beta, \\gamma$ have high sparsity.\n",
    "\n",
    "For this DGP, \n",
    "\\begin{align}\n",
    "\\theta(x) = \\exp(2\\cdot x_1).\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Treatment effect function\n",
    "def exp_te(x):\n",
    "    return np.exp(2*x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DGP constants\n",
    "np.random.seed(123)\n",
    "n = 2000\n",
    "n_w = 30\n",
    "support_size = 5\n",
    "n_x = 1\n",
    "# Outcome support\n",
    "support_Y = np.random.choice(np.arange(n_w), size=support_size, replace=False)\n",
    "coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
    "def epsilon_sample(n):\n",
    "    return np.random.uniform(-1, 1, size=n)\n",
    "# Treatment support\n",
    "support_T = support_Y\n",
    "coefs_T = np.random.uniform(0, 1, size=support_size)\n",
    "def eta_sample(n):\n",
    "    return np.random.uniform(-1, 1, size=n)\n",
    "\n",
    "# Generate controls, covariates, treatments and outcomes\n",
    "W = np.random.normal(0, 1, size=(n, n_w))\n",
    "X = np.random.uniform(0, 1, size=(n, n_x))\n",
    "# Heterogeneous treatment effects\n",
    "TE = np.array([exp_te(x_i) for x_i in X])\n",
    "T = np.dot(W[:, support_T], coefs_T) + eta_sample(n)\n",
    "Y = TE * T + np.dot(W[:, support_Y], coefs_Y) + epsilon_sample(n)\n",
    "\n",
    "Y_train, Y_val, T_train, T_val, X_train, X_val, W_train, W_val = train_test_split(Y, T, X, W, test_size=.2)\n",
    "# Generate test data\n",
    "X_test = np.array(list(product(np.arange(0, 1, 0.01), repeat=n_x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Train Estimator\n",
    "We train models in three different ways, and compare their performance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.1. Default Setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "est = LinearDML(model_y=RandomForestRegressor(),\n",
    "                model_t=RandomForestRegressor(),\n",
    "                random_state=123)\n",
    "est.fit(Y_train, T_train, X=X_train, W=W_train)\n",
    "te_pred = est.effect(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.2. Polynomial Features for Heterogeneity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The number of features in the final model (< 5) is too small for a sparse model. We recommend using the LinearDML estimator for this low-dimensional setting.\n"
     ]
    }
   ],
   "source": [
    "est1 = SparseLinearDML(model_y=RandomForestRegressor(),\n",
    "                       model_t=RandomForestRegressor(),\n",
    "                       featurizer=PolynomialFeatures(degree=3),\n",
    "                       random_state=123)\n",
    "est1.fit(Y_train, T_train, X=X_train, W=W_train)\n",
    "te_pred1 = est1.effect(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.3. Polynomial Features with regularization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "est2 = DML(model_y=RandomForestRegressor(),\n",
    "           model_t=RandomForestRegressor(),\n",
    "           model_final=Lasso(alpha=0.1, fit_intercept=False),\n",
    "           featurizer=PolynomialFeatures(degree=10),\n",
    "           random_state=123)\n",
    "est2.fit(Y_train, T_train, X=X_train, W=W_train)\n",
    "te_pred2 = est2.effect(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.4 Non-Parametric Heterogeneity with Causal Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "est3 = CausalForestDML(model_y=RandomForestRegressor(),\n",
    "                       model_t=RandomForestRegressor(),\n",
    "                       criterion='mse', n_estimators=1000,\n",
    "                       min_impurity_decrease=0.001,\n",
    "                       random_state=123)\n",
    "est3.tune(Y_train, T_train, X=X_train, W=W_train)\n",
    "est3.fit(Y_train, T_train, X=X_train, W=W_train)\n",
    "te_pred3 = est3.effect(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. Performance Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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T8fX1RavVYmpqytChQ3n77bcfGFdQiYLRo0fn7JWqWrVqTiJ1V506dViwYAGjR4+mZs2ajB07FktLS+bNm0f//v3Jzs6mUaNGjBkzJs/5BwwYwMmTJylTpkyh97NgwQLGjBlDWloaHh4ezJs3r9Bz8vLiiy/ywgsv4O/vj6+vb6HJh5+fHwMHDsTX1xdXV1datmyZ89nixYsZO3Ysn3/+OVqtloCAAHx8fJg+fTpDhgxh2rRpdOvWDYd8/qbz6quvEhERgZ+fH6qq4uzszNq1a+nUqRPBwcE0a9YMMGzGX7RoES4uLoXe33vvvUdoaCgmJia4uro+sOdLCCGEQfzixSjW1jj07l3gOMt69TCtVJHkrVtx7N2r4EkzU2DZEDAxhYELjWrInBkRATz+5Eox9tFTkSdWlP7A86qqvnLn62FAU1VVx+V3jr+/v3p/7abg4GDq1KlTKjE+KSIiIujevTtnz54t9hzdu3dnwoQJtG/fvgQje/zS0tKwsrJCURQCAwNZunSpUW8litL1LPx/KYQwTnZ8PJdat8Ghbx8qGvFG9q3/fU384sXUPLAfjZ1d3oNUFVaOgPNrYchqqN7WqFhufvklCStXUfvY0UfyhreiKMdUVfW//3hplmK4CjRVFMVaMdxheyC4FK/3TEpISKBWrVpYWVk9dYkVGN4w9PX1xdvbm19//ZVp06Y97pCEEELcI2nDRtSsLMre99JRfuw6d0LVakkp6A3sg7/AudXQ/mOjEyu4+6ag62MvnVNqjwVVVT2kKMpK4DiQDZwAZpfW9f7L3Nzcir1q5ejoyIULF0o4oidHy5Ytc/ZfCSGEePKkBAVh7uGBRc2aRo238vHBtEIFkrZuw+HOW+O5hO2C7ZOhTg9oMb5IsWRFXMGqnleRzikNpVpEVFXVj1VV9VRVtZ6qqkNVVc0s/CwhhBBC/Bfo09NJO3IE23v20BZGMTHBrlNHUvfuRZeSmvvD+AhY+TKUqw29fi10A/u91KwstJGRj32/FUiFdiGEEEIUU+qhQ6hZWdi0Mj65ArDv3Bk1K4uU3bv/PZiVCoEvgqqHgMVgkc9+rHxkRUaCXo+56+Nv5CzJlRBCCCGKJXXPXhQrK6wbNSrSeVYNGmDq7Ezy1q2GA6oK616D2+eh3x/gVL3IsWQ9IW8KgiRXQgghhCgGVVVJ2bsXmyZNMDE3L9K5iokJdh07krJnD/rUVNg/Hc6tgQ6fQI0OhZ2ep6zLEQCycvVfodFo8PX1xcvLCx8fH77//nv0ej1gaCCsKApz587NGX/ixAkUReG7774DYPjw4axcubJEY2rTpg33l614VAqq53XX3QbLBfnkk09yvkdPohUrVuDl5YWJickD3+uvvvqKGjVqULt2bbbe/ZtXEU2ZMoW///4bgOnTp+c06wbjvn/z58/n9ddfL9a1hRDiYWVFRKC9dq3IjwTvsnu+M2pmJinLf4G/P4V6faH5m8WP58oVNGXKoHF0LPYcJUWSKyNYWVlx8uRJzp07x/bt29m8eXOuNij169dn2bJlOV8HBgbi4+PzOEJ9JIra2qe06XS6Upm3Xr16rF69mlatWuU6fv78eQIDAzl37hx//fUX48aNK1YMU6dOzakGf39yJYQQT7rUO50+bO/7M9JY1g0boinjSNKyOVChHvT4uUgb2O/3JDRsvkuSqyJycXFh9uzZ/Pzzzzm9/6pVq0ZGRga3bt1CVVX++usvunTpYvScEREReHp68tJLL+Ht7U2/fv1yftDu2LGDBg0aUL9+fUaMGEFmZu4XLufOncuECRNyvp4zZw5vv/02ERER1KlTh5EjR+Ll5UWnTp1yeguePHmSpk2b4u3tTe/evYmPjwcMq2ETJkygVatW1KlThyNHjtCnTx9q1qzJRx99lHONu6sqKSkptG/fHj8/P+rXr29Ucc8vvviC2rVr06FDB0JDQ3OOh4WF8fzzz9OwYUNatmxJSEhIzvGmTZvSqFEjpkyZknPt3bt307ZtWwYPHkz9+vXR6XRMnDiRRo0a4e3tzaxZs3Lm/vbbb3OOf2xEgbu76tSpQ+3atR84vm7dOgICArCwsMDd3Z0aNWpw+PDhXGMOHz5Mnz59csZbWVmRlZVFRkYGHh4ewL8rmj/99BPXr1+nbdu2tG37bz2XDz/8EB8fH5o2bVpgQ+z7jR07Fn9/f7y8vHLd73vvvUfdunXx9vbmnXfeAQyrc/Xq1cPHxycniczIyODll1+mfv36NGjQgF0F1aIRQjyzUoL2YO7ujnmVKsU6X8lKxq5SCimRGvQ954K59UPF8yQlV6XduLlEfX34a0LiQkp0Ts+ynrzb+N0inePh4YFer8/pUQfQr18/VqxYQYMGDfDz88vV9NgYoaGhzJ07lxYtWjBixAh+/fVXXn/9dYYPH86OHTuoVasWw4YN47fffstpowMQEBCAt7c333zzDWZmZsybNy8nsbh48SJLly5lzpw5DBgwgFWrVjFkyBCGDRvGjBkzaN26NVOmTOHTTz9l+vTpAJibm7Nnzx5+/PFHevbsybFjxyhbtizVq1dnwoQJODk55Vzb0tKSNWvWYG9vT0xMDE2bNqVHjx75Fm87duwYgYGBnDhxguzsbPz8/GjY0NDdfNSoUcycOZOaNWty6NAhxo0bx86dOxk/fjzjx49n0KBBD7SfOXz4MGfPnsXd3Z3Zs2fj4ODAkSNHyMzMpEWLFnTq1ImLFy9y8eJFDh8+jKqq9OjRgz179tCqVasiNW6+V1RUFE2bNs35ukqVKkRFReUa4+fnx4kTJwBDD8N69epx5MgRsrOzadKkSa6xb775Jt9//z27du2iXLlyAKSmptK0aVO++OILJk2axJw5c3IluAX54osvKFu2LDqdjvbt23P69GmqVKnCmjVrCAkJQVEUEhISAMPq2datW6lcuXLOsV9++QWAM2fOEBISQqdOnbhw4QKWlpZGXV8I8fS7W4KhzKCAYk6gg1WvYF8+moRzZUg5fQX7ig/+Zdbo6VJTyb59+4lJrmTlqpjubxs0YMAAVqxYwdKlSwttnpyXqlWr0uJOJ/EhQ4awb98+QkNDcXd3p1atWgC89NJL7NmzJ9d5NjY2tGvXjo0bNxISEoJWq6V+/foAuLu74+vrC0DDhg2JiIggMTGRhISEnGbN98/Z405Bt/r16+Pl5UXFihWxsLDAw8ODa9euPfA9+OCDD/D29qZDhw5ERUUVuMKyd+9eevfujbW1Nfb29jnXSklJ4cCBA/Tv3x9fX19Gjx7NjRs3ADh48CD9+/cHYPB91X8bN26Mu7s7ANu2bePPP//E19eXJk2aEBsby8WLF9m2bRvbtm3LSXpDQkJyGjIX1rg5P3m1jLo/oTQ1NaVGjRoEBwdz+PBh3n77bfbs2cPevXtz9VTMj7m5Od27dwf+/XdnrOXLl+Pn50eDBg04d+4c58+fx97eHktLS1599VVWr16NtbXhb4gtWrRg+PDhzJkzJ+fR5r59+xg6dCgAnp6euLq6PtWFaoUQRZdTgqFl8R4Jsn0KXPob6+FfoilXjsT16x8qnielYfNd/6mVq6KuMJWW8PBwNBoNLi4uBAcbOvpUqFABMzMztm/fzo8//ljkfUn3/3BWFCXPH+J5efXVV/nyyy/x9PTk5Zdfzjl+7+qZRqPJeSxYkLvnmJiY5DrfxMSE7OzsXGMXL15MdHQ0x44dw8zMDDc3NzIyMgqcP69VLb1ej6OjIydPniw0vnvZ2Njk/F5VVWbMmEHnzp1zjdm6dSvvv/8+o0ePfuD84q5cValSJVeiGRkZSaVKlfKcf8uWLZiZmdGhQweGDx+OTqczahO/mZlZzvdKo9E88L3Pz+XLl/nuu+84cuQIZcqUYfjw4WRkZGBqasrhw4fZsWMHgYGB/Pzzz+zcuZOZM2dy6NAhNm3ahK+vLydPnjT6vzshxLPr3xIMD7TVK9zJJXDwZ2g8CqXxCBx6xhK34E+yY2IwvbN6X1RPUhkGkJWrIouOjmbMmDG8/vrrDyQKU6dO5euvv0aj0RR53qtXr3Lw4EEAli5dynPPPYenpycRERFcunQJgIULF+asON2rSZMmXLt2jSVLlhS6aubg4ECZMmXYe2cjYn5zGiMxMREXFxfMzMzYtWsXV+78zSE/rVq1Ys2aNaSnp5OcnMyGDRsAsLe3x93dnRUrVgCGROluy5umTZuyatUqwPCiQH46d+7Mb7/9hlarBeDChQukpqbSuXNn/vjjD1JSUgDDI727j3OLu3LVo0cPAgMDyczM5PLly1y8eJHGjRvneb/Tp0+nWbNmODs7ExsbS0hICF5eD7ZmsLOzyzPRK6qkpCRsbGxwcHDg1q1bbNmyBTCsDiYmJtK1a1emT5+ek8iGhYXRpEkTpk6dSrly5bh27RqtWrVi8eLFgOH7ePXq1Tz3ngkhnk25SjAUcQsM147AhvHg3go6fwmAY58+kJ1N4voNxY4p825y5Vqt2HOUpP/UytXjkp6ejq+vL1qtFlNTU4YOHcrbb7/9wLiCShSMHj06Z69U1apVcxKpu+rUqcOCBQsYPXo0NWvWZOzYsVhaWjJv3jz69+9PdnY2jRo1YsyYMXnOP2DAAE6ePEmZMmUKvZ8FCxYwZswY0tLS8PDwYN68eYWek5cXX3yRF154AX9/f3x9ffH09CxwvJ+fHwMHDsTX1xdXV9dcj8cWL17M2LFj+fzzz9FqtQQEBODj48P06dMZMmQI06ZNo1u3bjg4OOQ596uvvkpERAR+fn6oqoqzszNr166lU6dOBAcH06xZM8CwGX/RokW4uLgUen9r1qzhjTfeIDo6mm7duuHr68vWrVvx8vJiwIAB1K1bF1NTU3755Zc8E+omTZpw69atnI3i3t7euLi45Ll6N2rUKLp06ULFihWLtIF8/vz5rF27Nufrf/75hwYNGuDl5YWHh0fOo+bk5GR69uxJRkYGqqryww8/ADBx4kQuXryIqqq0b98eHx8fPD09GTNmDPXr18fU1JT58+cXeQ+hEOLpdbcEQ9mXhxftxMRIWPYi2FeG/gtAYwaARfXqWPn4kLB6FWVfHl6spstZERGYVqqIyROyN1R5kh4B+Pv7q/fXEwoODqZOnTqPKaJHIyIigu7duxe7eTNA9+7dmTBhAu3bty/ByB6/tLQ0rKysUBSFwMBAli5datRbiaJ0PQv/Xwoh8hb355/c+vIrqv+93fg3BTNTYN7zEBcBr24Hl9x/fsQvX87NKR/jtnwZVt7eRY7p8sCBmFhb41rMxYLiUhTlmKqqDzwblceC/3EJCQnUqlULKyurpy6xAsMbhr6+vnh7e/Prr78ybdq0xx2SEEI801L27C1aCQa9HtaMhlvnoP/8BxIrAPuuXVEsLUlYtbrI8aiqStblJ6cMA8hjwSeCm5tbsVetHB0dn+o3uVq2bJmz/0oIIcTjpU9PJ+3w4aKVYNg5FUI2wvNfQ82897RqbG2x79yJpE2bKP/eu5hYWRk9vS4hAX1SEhZPUHIlK1dCCCGEMEqRSzCcXAL7fgD/EdDkwbe27+XQpy/6lBSS77QFM1ZOT0FJroQQQgjxX5O4dh0m1tbGlWC4chDWvwnuraHLN4W2trFu5I9ZlSpFfjT4pJVhAEmuhBBCCGGE9DNnSP7rL8oOf6nwEgxxlw1vBpZxhQH/vhlYEMXEBIc+vUn75x+yIqMKHX9XVkQEmJpiVrmy0eeUNkmuhBBCCFEgVVW5/d00NGXKUHbEiIIHp8fDkgGGFjeDloFV4SWC7nLs1QsUhcQ1a4w+JysiAvOqVVFMn5xt5JJcGUGj0eDr65vzqyitSIpi9+7duSq7f/LJJ1SuXBlfX19q1qxJnz59OH/+fM7nbdq0oVq1arkqavfq1SunuXFERAT16tUrlViFEEI8O1L37SPt0CHKjRuH5s7PmDxlZ8HyYYaVq4AlUK5Gka5jVqkSNs2akbhmDapeb9Q5T1LD5rskuTKClZVVrgrebkb+SzS2Zcld9ydXABMmTODkyZNcvHiRgQMH0q5dO6Kjo3M+d3R0ZP/+/YChLMPdnnxCCCFESVD1em5/Nw2zqlUpM3BAAQNV2DQBLu+BHjPArUWxrufQtw/a69dJO3TIqNiyrlyR5OppcfLkSZo2bYq3tze9e/cmPj4eMKwmffDBB7Ru3Zoff/yRY8eO0bp1axo2bEjnzp1zkp+ffvqJunXr4u3tTUBAABEREcycOZMffvgBX1/fnPY09xo4cCCdOnViyZIlOccCAgJy2sKsXr2aPn36PIK7F0II8axI2rCBzNBQnN8aj2Junv/AfT/AiUXQahL4FtyKrSB2HTpg4uBAfOCyQsdm37qFmpn5xCVXT84DSiPc/PJLMoNDSnROizqeVPjggwLH3G1/A+Du7s6aNWsYNmwYM2bMoHXr1kyZMoVPP/2U6dOnA4YVpKCgILRaLa1bt2bdunU4OzuzbNkyPvzwQ/744w/+97//cfnyZSwsLEhISMDR0ZExY8Zga2vLO++8A8COHTseiMXPz4+QkH+/B+3bt2fkyJHodDoCAwOZPXs2n332Wcl8c4QQQjzT9JmZ3P7xRyy9vLDv0iX/gWdXw45PoX5/aFvwz9TCmFhYUKZ/P2L/mEdWZBTmVfLfqJ7zpqCra86xmBgoZv/nEiMrV0a497HgmjVrSExMJCEhIafh8UsvvcSePXtyxg8cOBCA0NBQzp49S8eOHfH19eXzzz8nMjISMPSZe/HFF1m0aBGmRdiEd3+7Io1Gw3PPPceyZctIT083+pGlEEIIUZj4JUvJvn4Dl3f+D8Ukn5Th2hFYOxaqNoUePxdacsEYZYYMARMT4hf+WeC4nOTK3Q2AM2egTh345ZeHDuGh/KdWrgpbYXpS2NjYAIZEyMvL64EmzQCbNm1iz549rF+/ns8++4xz584ZNfeJEyfw989dXyQgIIDevXvzySefPHTsQgghBIAuKYnYmTOxadECm2bN8h4UGwZLB4JdRcMGdrOSaZxsVqEC9l26kLBiJeVefx2NnV2e47IiIlCsrDB1cQEgLAxsbKBz5xIJo9hk5aoYHBwcKFOmTM6+qIULF+asYt2rdu3aREdH5yRXWq2Wc+fOodfruXbtGm3btuWbb74hISGBlJQU7OzsSE5Ozve6q1atYtu2bQwalPtZdsuWLXn//fcfOC6EEEIUV+zvc9ElJuLyzv/lPSA1Bhb3M2xkH7IKbJxK9PplX3oJfVoaCStW5vm5LiGBxM2bsapXj8xMw2pZr14QEgI1ivaSYomT5KqYFixYwMSJE/H29ubkyZNMmTLlgTHm5uasXLmSd999Fx8fH3x9fTlw4AA6nY4hQ4ZQv359GjRowIQJE3B0dOSFF15gzZo1uTa0393gXrNmTRYtWsTOnTtxdnbOdR1FUXjnnXcol8dD5tDQUKpUqZLza8WKFaXzDRFCCPHUyI6JIW7hQuy7dcOyzoONltGmw9IASLoOg5eBU/USj8GqnhfWjRoRt3Ahqlb7wOc3v/wSXXwCiYM+plYtWLvWcNyyZBbPHopy/x6ex8nf3189evRormPBwcHUyetfrBDisZH/L4V4ut366iviFi3GY+MGLNzdc3+o1xlqWYVsMlRfr9uz1OJI3rmLyHHjqDTtOxy6dbvn+E4ix71GUv8P6D13KDod7N5t2G/1KCmKckxV1Qd6AcnKlRBCCCFyaG/eJH5pIA69ej6YWAFs+whCNkLnL0s1sQKwbdMac1dX4ubNz3mhS5eQwI2PP+Z6lXb0+WMIWi3s3PnoE6uCSHIlhBBCiBwxM2eiqirlxo578MODv8I/v0KTsdAsj89LmGJiQtnhL5Fx9izpx48DhlW129EmDD0zHa1WYdcu8PIq9VCKRJIrIYQQQgCQFRlJwspVlOnf78H6UmdXwdb3oc4L0PmLRxaTQ69eaBwciJ03j+Sdu0hct55aY/sx5jUzdu6EJ7HL23+qFIMQQgjxX3P7+x9A1ePyf/m8dfcEifnlVxSNBqfRY3J/cHkPrBkD1ZpBnzlgonlkMZlYWeE4KIDYWbM5uz8WTcV2eI4dzZQCisU/bpJcCSGEEKVEn5VF3KJFqGlpWDdtim2L4vXbexQyw8NJXLeOssOGYVbe5d8Pbp6FwBehrAcMWgpmVo88tjKDB3P4l10MPzODyjVtOGn2BGdWyGNBIYQQotSkHz2KmpaGYmXFzU8+RZ+e/rhDylfMzz+jWFriNPLVfw8mXDPUsjK3NdSysirzWGILve3CiOhlmNrbsmyNVUkUgS9VklwVIDY2Fl9fX3x9falQoQKVK1fO+TorK6tUrx0SEoKvry8NGjQgLCyMn376iTp16vDiiy8Wea7p06eTlpZWClEKIYQoSMqevShmZlSZ/gPaa9eI/vnnxx1SnjJCQ0navIWyQ4di6nSnGGhaHCzqC1lpMGQlOFR5LLGdPAlt2oCZjSVBBy3x9HwsYRSJJFcFcHJyyukpOGbMGCZMmJDztbm5OdnZ2aV27bVr19KzZ09OnDhB9erV+fXXX9m8eTOLFy8u8lySXAkhxOORsncv1o38sW3dGsf+/Yibv4CM8+cfd1gPiP7xJ0zs7HAa8bLhQFYaLB0E8ZchYDGUf3yv4332maGlzZ49ULv2YwujSGTPVRENHz6csmXLcuLECfz8/LCzs8PW1pZ33nkHgHr16rFx40bc3NxYtGgRP/30E1lZWTRp0oRff/0VjSb3JsBjx47x9ttvk5KSQrly5Zg/fz4nTpxg+vTpaDQa9uzZQ+3atQkPD6dHjx6MGDGCUaNG8cYbb3DmzBmys7P55JNP6NmzJzqdjnfffZetW7eiKAojR45EVVWuX79O27ZtKVeuHLt27Xoc3zYhhHjmaKOiyAoLw7FfPwBc3nmH5F27ufHRZNyWL0MxfTJ+BKcdP0HKzp04j38TjYMD6LSwYjhcOwT954N7y8cSl6oaekAvWABxcVCt2mMJo1iejH+zRdCmzYPHBgyAceMgLQ26dn3w8+HDDb9iYuDOf+M5du8uegwXLlzg77//RqPR5NssOTg4mGXLlrF//37MzMwYN24cixcvZtiwYTljtFotb7zxBuvWrcPZ2Zlly5bx4Ycf8scffzBmzJhcSdtff/3Frl27KFeuHB988AHt2rXjjz/+ICEhgcaNG9OhQwf+/PNPLl++zIkTJzA1NSUuLo6yZcvy/fff55wrhBDi0Ui508bMtpUhOdE4OFDhow+JemsCcQv+xOmVEY8zPABUVeX299PQlCtH2ZdeMmQ069+Ei1uh2/fg1euxxLVnD3zxBaxcCXZ2YGv7WMIotv9ccvUk6N+//wMrUPfbsWMHx44do1GjRgCkp6fj4uKSa0xoaChnz56lY8eOAOh0OipWrFjo9bdt28b69ev57rvvAMjIyODq1av8/fffjBkzBtM7fxsqW7Zske9NCCFEyUjZuw+zSpUw9/DIOWbXuTO2bdsSPWMGdp06Yl616mOMEFL37CH96DHKT5mMibU1bJsMp5ZA2w+h0SuPJabt26FnT3B1hdRUQ3JVVPGpWZSxeXxvFP7nkquCVpqsrQv+vFy54q1U3c/Gxibn96ampuj1+pyvMzIyAMPfBl566SW++uqrfOdRVRUvLy8OHjxYpOurqsqqVauofd/DZ1VVUZ70VyiEEOIZoGZlkXbwIPY9Xsj157KiKFSYMpnwbt259fkXVJ01s2Sup6okLF9B1tUr6GLjyI6PQxcbhz4lBadXX8l5NJnrHL2e29//gFm1apTp3x/2/wgHfoJGI6HVxBKJq6jWr4f+/cHT05Bk3bcmUSBVVdl7MYaZQWGER6cSNKkNFqaPrh7XvWRD+0Nyc3Pj+J2S/MePH+fy5csAtG/fnpUrV3L79m0A4uLiuHLlSq5za9euTXR0dE5ypdVqOXfuXKHX7Ny5MzNmzMjps3TixAkAOnXqxMyZM3M22sfFxQFgZ2dHcnLyw96qEEIII6UdP44+LQ3bVq0e+MysYkXKvvoKKUFBZF29WiLXywwO5ubHHxO/cBGphw6hi4lFU6YMJjY23Jg8haQtWx44J2njRjJDQ3F+802Ucytg+xTw6gNdvuFx1DpYswb69AFfX9i1y/jEKlunZ8Op63SfsY9hfxwmLDqFEc+5cedH5GMhydVD6tu3L3Fxcfj6+vLbb79Rq1YtAOrWrcvnn39Op06d8Pb2pmPHjty4cSPXuebm5qxcuZJ3330XHx8ffH19OXDgQKHXnDx5MlqtFm9vb+rVq8fkyZMBePXVV6lWrRre3t74+PiwZMkSAEaNGkWXLl1o27ZtCd+9EEKIvKTs2QtmZlg3bpLn5459+4GJCQmrVpfI9TIvXQLAfc1qau7aifuqlVSbMxvXRQux8vMjatK7pN7z80XNyiL6x5+wqFsHe3cdrHsdPNpA75lg8nhSA29vw77o7dvBmF0tGVodC/+5QrtpQbyx9ATpWh3f9PVmz6S2jGpVHUuzx7NqBaCojzO1u4+/v7969OjRXMeCg4Op8yS1uhZCyP+XQhQi/IUX0DiVw3X+vHzHXBszloxz56ixa+dDvzl4+/sfiP3jDzxPHEcxM8v1mS4xkStDh5EVGYnrgvlY1a9P3MJF3PriC6p+9ga2IR9DRR8YuhYsHv3O8R07oF074xfLEtO0LDp0hXn7LxOTkoVPVUfGtq5Op7rlMTF5tCtuiqIcU1XV//7jsnIlhBBClCDtjRtkXryEbcuCSxg49u9HdnS0YZXrIWWGhWHu5vpAYgWGtxSrzpmDaZkyXBs1mvSz54j57TesfetgE/o5ONWAwcsfeWKlqoY3Ajt0gPnzCx9/MzGDLzadp/n/dvDt1lC8KjkQOKopa8c15/l6FR55YlWQ/9yGdiGEEOJJdjdZuluCIT+2rVqhcS5HwsqV2LV7uG0bWZcuYVFA6XKz8i5Um/s7EYNfJCIgALKzcWl2HcXOBYauAetH+3a5qsLEiTBtGgwdaviVn0u3k5kVFM7ak1Ho9Cov+FRidKvq1K1k/+gCLiJJroQQQogSlLJ3D6YVK2Jeo0aB4xQzMxx79Sb2jz/Q3rqdu1lyEegzM8m6dg37bt0KHGfu5kbVObO5OnQoNlW1WFWygGFrwa5Csa5bXDodjB4Nc+fC66/Djz/mvc3r+NV4Zu4OY9v5W1iamTCocTVGtvSgalnrRxpvcfwnkispMSDEk+NJ2qcpxJNGzcoi7cBB7Lt3N+rnlmPfPsTOmUPi2rWUGz2qWNfMiogAvR6LGtULHWtVtQzVB2jR6DNg6BYo41asaz6MU6fgzz9h8mT49NPce61UVWV3aDS/BYVx+HIcDlZmvNmuBi81d8PJ1uKRx1pcT3xyZWlpSWxsLE5OTpJgCfGYqapKbGwslpaWjzsUIZ5IacdP3CnBYFzLGHM3N6wbNyZh1SqcRr6KUow39e6+KWheveCVMlKi4c+emOrj4aV14PJoX0rR6UCjAT8/OH8e7l3Y0+r0bDx9nVlB4YTcTKaSgyWTu9cloFFVbCye+FTlAU98xFWqVCEyMpLo6OjHHYoQAsNfeKpUqfK4wxDiiZSyd4+hBEPTZkaf49ivL9cnvUvakaPYNGlc5GtmhYWBiQnm7m75D0qPh4W9IeEaDF0NlRsW+ToPIy4OXngBXnsNBg/+N7FKy8pm+ZFrzNl7maiEdGqVt2Vafx96+FbCTPPffefuiU+uzMzMcHd3f9xhCCGEEIVK3bcfaz8/NLY2hQ++w65TJ0w++5yElSuLlVxlXgrDvGpVTMzzafeSmQyL+kFMKAwKBNfmRb7Gw4iKgs6d4dIlsLIyHItPzWLBwQgWHIggPk2Lv2sZPu3hRTtPlyfqrb/ieuKTKyGEEOK/QJ+eTubFi5QbO7ZI55lYWuLwwgskrFyJ7qMP0Tg4FOn8zLCw/DfPZ6XBkgC4fgIGLoQa7Ys098O6cAE6doT4ePjrL6jpm86nG8IJPHyNdK2ODnVcGNO6Ov5uT1cvXEmuhBBCiBKQGRoKej2WdYu+l8mxfz/ilywhccNGyg550ejzVK2WrCtXsGufR9KUnQnLh8KV/dD3d/As+G3Cknb7Njz3nOH381emsv7WRdZ/cx2AHr6VGNO6OrXKF6Mr83+AJFdCCCFECcgIDgbAshjdCyzr1MHSy4uElSuLlFxlXb0K2dkPvimYnQXLX4JLf0OPGVD/wcbNpc3FBQa9mkaU/SXe3nkNa3MNw5q58UpLdyo7Wj3yeB4lSa6EEEKIEpBxPhgTBwdMK1Ys1vn2XZ7n9nfTyI6Lw9SY5noY9lsBmFe/J7nSZcOqV+DCFug2DfyGFSue4lqyRCXBLI6dMaEc1cdTJsuMCR1qMayZK2Vs8tkX9pSR5EoIIYQoARkhIVjWqVPsskGW9eob5jl3rtDWOXdlhl0CRcHCw8NwQK+DNaMgeD08/z9o9GqxYimOrGw9Iycm8ed0R6xrZ+I7PINPe3gxwL8qVuaPpolymjaNjeEbORNzhs9afPZIrpkXSa6EEEKIh6RmZ5N54QJlBg8u9hyWXnWBoiVXWZfCMKtcGRMrK0NitXYcnF0FHadC06JtrC+u1Mxslhy6xscfmnBjnyvlfaL5aaZK70ZtHlk5hStJVwgMCWTdpXUka5OpU7YOKVkp2Jo/+kbUIMmVEEII8dAyw8NRMzOLtZn9Lo2dHWau1Ug/e9b464aFYVG9Ouj1sGE8nA6Eth9Bi/HFjsNYsSmZLDgQwby9V7m8ui6p5yvTc3AaKxeUw9S09Msp6FU9+6L2sSRkCfuj9mOqmNLRrSODPQfj4+zzWAuPS3IlhBBCPKTMh9jMfi8rr3qknThh1Fg1O5usy5exadEcNk2AEwuh1URoPfGhYijMtbg05uwNZ/nRa2Ro9XSoXR6H8i50GwLvvWdNaec0SVlJrL24lsDQQK4lX6OcVTnG+YyjX61+OFs7l+7FjSTJlRBCCPGQMs4Ho1hYYO7m9lDzWHp5kbR5s1Gb2rWRkahZWVikHIZj26Hl/0HbDx/q+gU5fz2JmUFhbDpzAxMFOlRzY1gLV5rVs0E3zNDapjRdjL/I0pClbAzfSHp2Og1cGvBGgzfoUK0DZhqz0r14EUlyJYQQQjykjOBgLGrXRjF9uB+rll5ehvmM2HeVedHQU9Aifjf0fBvaTaakl41UVeVgeCwzg8LZcyEaG3MNI1q40ayMB8MGWBJaC3bsKL3EKlufza5ru1gaspQjN49gobGgq3tXBnkOoo7To+2NWBSSXAkhhBAPQVVVMkJCsO/S5aHnMnpTu15P5uafADDvPBraTynRxEqvV9l2/ia/BYVz6loC5WzNmdi5NkOauHLsHzN6dwFbW/jhhxLP5wCIy4hj1YVVLAtdxq20W1S2rczbDd+md43eOFo6lvwFS5gkV0IIIcRD0EZdR5+U9ND7reDfTe0Z587lP0hVYfM7ZJ4/hWmZcmi6fV5iGU5mto61J6KYFRROeEwq1cpa83mvevRrWAVLMw2LF8PLL0OtWrBlC1StWiKXzXE25ixLQ5ay5fIWtHotzSo248MmH9KqSis0Jo+mnENJkORKCCGEeAgZwecBHupNwXtZedUj7WQ+m9r1esPm9WPzydJ7YeHlVSKJVXKGliWHrvLH/svcSsrEq5I9MwY1oEu9CpjeKaeQmQmffw4tWsCaNeDo+NCXBSBLl8XWiK0sDVnKmZgzWJta069WPwJqB+Dh6FEyF3nEJLkSQgghHkJmcDCYmGBRs2aJzJfvpna9Dja8CScWoTafQObadZRpVz3/iYwQnZzJvP2XWfjPFZIzsmlRw4nv+vvwXI1yOaUMsrIMi2UWFob9VeXKgXkJFFq/mXqT5aHLWXVxFXEZcbjZu/F+4/fpUb3HY6tPVVIkuRJCCCEeQsb5YMw93A2FPEtAnpva7xYIPR0Ird9DW3MoasYyzO/vKWikiJhUZu8NZ+WxSLQ6PV3qVWBM6+p4V3HMNS4hAfr2hQoVYNEiqFTpIW4Mw/60o7eOsjRkKTuv7kSv6mldtTWDPAfRrGKzx1qbqiSVanKlKIoj8DtQD1CBEaqqHizNawohhBCPUkZwMNaNG5fYfA9satdlG1ranF1lKBDaeiJZQUEAWFSvUaS5z0Yl8ltQGFvO3MDUxIS+DaswqpUH7uVsHhgbEQFdu8KlSzB37sM9fbzblmZpyFIuJVzCwcKBYV7DGFh7IJVtKxd/4idUaa9c/Qj8papqP0VRzAHrUr6eEEII8chkx8WRfetWiWxmvyvXpnad1tCE+fw66PAJPDcB+Ldhs0X1wvckqarK/kuxzAwKY9+lGOwsTBnVqjojWrjhYm+Z5zkHD0LPnqDVwrZt0KZN8e7l/rY0nmU9mdp8Kl3cu2Bpmve1nwalllwpimIPtAKGA6iqmgVkldb1hBBCiEct425l9hLazH6XlZeXoVL78mEQuhk6fQHNX8/5PDMsDFNnZzQODvnOodOr/HX2JjODwjgTlYiznQXvdfFkcJNq2FvmX3QzPR369AEHB9i4EWrXLlrsT3JbmkelNFeuPIBoYJ6iKD7AMWC8qqqp9w5SFGUUMAqgWrVqpRiOEEIIUbJy2t54epbovJaetUjavIXs0ycx7fUtNBmV+7phlzCvnvd+qwytjlXHI5mzJ5yI2DTcy9nwvz716e1XGQvT/MsZqKrhn1ZWsHYt1KgBTk7Gx5yYmcjaS2sJDAkkMiUSZytnxvmOo1/NJ6ctzaNSmsmVKeAHvKGq6iFFUX4E3gMm3ztIVdXZwGwAf39/tRTjEUIIIUpUxvlgTCtVRFNSdQkAMpOxvL7MMH+tt7C9L7FSVZWsS2E49OqV63hiupZF/1xh3v4IYlIy8aniwG8v+tHJqwIak4JXizIyYMQI8PGBd9+FJk2MDzc0LpSlIUvZFL6JDF0Gfi5+jPcbT3vX9piZPFltaR6V0kyuIoFIVVUP3fl6JYbkSgghhHgqZAQHY1mnbslNmB4Pi/piqT0NuJCRWZ77ixJk37qFPjUViztvCt5KyuCPfZdZfOgqKZnZtKxZjrGtfWlW3cmoR3A3b0Lv3vDPP4bkyhhavZYdV3ewNHgpx28fx1JjSTePbgR4BuBZtmRX8f6LSi25UlX1pqIo1xRFqa2qaijQHjhfWtcTQgghHiV9aipZERHYd+tWMhOmxsDCXhAdimbon5gd/SnPSu3JO3YAEONchf+tPM2aE1Fk6/V0rV+RMa2rU69y/vuw7nfiBPToAXFxsHKloexCQWLSY1h5YSUrQldwO/02lW0r847/O/Sq0QsHC+Ov+7Qr7bcF3wAW33lTMBx4uZSvJ4QQQjwSGRcugKqWzGb2xChDYpVwFQYthRodsPLaQdrJk7mGZYaFcfObb7nqXo9xfydgZprCgEZVGNnSA1enB8spFCQ2Flq3NlRa37cPGjTIe5yqqpyKPsXSkKVsu7KNbH02LSq34GPPj2lRqcV/qi3No1KqyZWqqicB/9K8hhBCCPE4ZJTUZvbYMPizl+GR4JDV4NbCMK9XPcOm9vh4NI6OBJ2LQh07FivVlC+9+zOubS2Gt3CjnK1FsS7r5AS//w6tWhmKhN4vIzuDLZe3sDRkKcFxwdia2RJQO4CBtQfi5uBW/Pt9BkiFdiGEEKIYMoOD0Tg4YFqxYvEnuXkWFvYGVQfDN0Clf5eP7lZq37U+iO8TnHhu2yL6RF/j/Osfs3lkH2wtiv4jPC0NRo6EwYOhWzcYMODBMZHJkSwPXc7qS6tJzEykhmMNJjedTHeP7libSblKY0hyJYQQQhRDxvlgLOrWKX7dpmuHYXE/MLOBYZvAuVbOR+lZOtam2uIH/L12DzXcatAnbA/2gwfT9/WAYl3u6lXo1QtOnoT7C8rrVT3/XP+HpSFLCYoMwkQxoV21dgzyHIR/ef9nojZVSZLkSgghhCgiXUoqmRcvUmbIkOJNELYLAl8Eu/IwdC2UcQUgIS2LPw9eYf6BCOJSs1jo4EIAUdgcPoxpzZpUnDSxWJfbu9ewWT0zEzZsMKxaASRnJbM+bD2BIYFEJEVQ1rIsI71H0r9WfyrY5PGsUBhFkishhBCiiBJWrkDNysK+c6ein3x2NaweBc61DXus7MpzPSGdufsus/TwVdKydLTzdGFM6+pUymxI8pYt6C0sqDTvD0wsi94y5swZaNcOPDxg3Trw9ISL8RcJDAlkQ/gG0rPT8Xb25svnvqSzW2fMNeZFvyeRiyRXQgghnnrJu3YRv3AhDr17Y9+5M4p58RMIVaslbsGfWPv7Y2VsYai7jvwOm96Bak1hUCCXkjXM/OsUa09EoQI9fCoxurUHnhXsAYitX5/kLVtweXcSlrVqFTx3PurVg2nTYPCQbE4k7+KbrUs5cvMI5ibmdHHvwiDPQXiV8yrW3CJvklwJIYR46sXOnkP6yZOkHjjI7W++pczgQTgOHIhp2bJFnitpyxayb9ygwsdTjD9JVSHoa9j9FdTqwvEmP/Dr8kv8HXwLSzMThjR15dWW7lQpk3vDuGP/fphVroxdp45FivHmTUPF9WnTwNktBovWqwjYsYJbabeoaFOR8X7j6VuzL2UsyxRpXmEcRVWfnI4z/v7+6tGjRx93GEIIIZ4iWZFRhHXogPNbb2HpVZe4BX+Sum8firk5Dn16U+GDD4xeyVJVlcs9e4Gqx33dOhQTk8JP0utgyyQ48js33PvwVtoIDl1JwtHajJeaufFSczfK2pTco7h//oG+fVXiEvR0m7KQsEo/kq3PplnFZgzyHESrKq2kNlUJURTlmKqqD5SckpUrIYQQT7WkzZsBsO/eDfMqVbBt2ZLMS5eIW/AnCYHLMLG0ovx77xo1V+q+fWReuEDFr74yLrHKzkS/ejQm59ewwrw3E4P7Uskhiynd6xLQuCrW5iX7Y/iX37S8NV6DhdNtqrw/jsiq1xlQfQABngG4O7iX6LVE/iS5EkII8VRL2rgRqwYNMK9SJeeYRY0aVPxsKoqZKXHz52PdtAl2bdoUOlfs73MxLV8eh25dCx2blhRH4rz+VIw/ypfaQQQ5DOb75z14wacSZhojErMiiEqJYtIPp1kypQu29fbR6t1ZDPMfSPfq3bExK1rldvHwJLkSQgjx1MoIvUDmhQuUn/xRnp+7vPsuaceOc+P9D7Bcuwaz8uXznSv9zBnSDh3CZdKkAh8jxqVmsXLXYdocHYu7GsWPjhNp8vxI3vd0KdF6UXdrUy0JXsqeqCCoZEHbN9P4clI1mlT6U2pTPUaSXAkhhHhqJW3aBBoN9s8/n+fnJhYWVP7hey737cf1iZOoNu8PFE3e+5Fi5/6Bia0tjgP65/n5tbg05u67zOEjB5lj8iVOJmlc7jSP8c17ltj9gKE21bpL6wgMDeTcPxWIXT2BD3+vz0v+PajwitSmehJIciWEEOKppKoqSZs2YdOsGaZOTvmOs/DwoMLkydz44ANiZs7E+bXXHhiTdfUqydu24fTKCDS2trk+C76RxKygMDacvoG/SSgrLaZhbm6J6bCt1KpYxFINBbgQf4HAkEA2hm8kTZuO2e6PuLpwAJ51VAZU86KCPP17YkhyJYQQ4qmUfvIk2qgoyr3xeqFjHXr3IvWfg8T88is2jRtj3ahRrs/j5s9H0WgoM2QoYEjcDl+OY2ZQGLtCo7Ex1/C1Zzh9r3yF4lgVhqyCMm4PfQ9avZadV3cSGBLI0VtHsdBY0Na5F8d/epOdm+0ZOBB+/13hvnxPPGaSXAkhhHgqJW3chGJhgV2HDoWOVRSFClM+Jv3UKSInvI2Vrw9qWhr6tHT0aWlkXr6MQ88eaJyd2XruJjODwjhxNQEnG3Pe6ViTVzSbsdr9CVRtDAFLwSb/lTJjxKTHsOLCClaGruR2+m0q21bm7YZv07tGb8aPdmTPNpg+Hd58E2Rr1ZOn0DpXiqL0V1V1RWHHSoLUuRJCCFES1OxsLrZqjXXjxlSZ/oPR52UEB3Nj8hRUrRYTKytMrK1RrK1QbO040qYfP59LJiw6laplrRjV0oN+DSphtfNDODwb6vaE3rPAzKp4Masqp6JPsSRkCduvbCdbn02Lyi0YVHsQz1V+Dm2WBktLuHEDwsOhRYtiXUaUoIepc/U+cH8ildcxIYQQ4omQevAfdHFxOHTvVqTzLOvUwX3lvz/eUjKzCTx8ld/3Xubm7pvUqWjPT4Ma0LVeBUx16bDqJQjdDM3fgA5TwZjaV/dJz05nc/hmAkMDCYkLwc7MjoDaAQR4BuBq70paGox8Fa5eha1boWJFwy/x5Mo3uVIUpQvQFaisKMpP93xkD2SXdmBCCCFEcSVt3IiJnR02rVoV6/yYlEzm74/gz4MRJGVk09SjLF/386ZVzXKGEgfJt2BpANw4CV2/g8Yji3yNq0lXWRa6jDWX1pCclUzNMjWZ3HQy3T26Y21maINz8SL06wenT8PkycW6FfEYFLRydR04CvQAjt1zPBmYUJpBCSGEEMWlz8ggeft27Lo8j0kRGzRfjU1j9t4wVhyNJEunp3PdCoxpUx3fqo7/Drp1DpYMhLRYGLgYPAsvKJoTm6pnX9Q+loYsZV/UPkwVU9q7tmeQ5yD8XPxy1aZatgxefRXMzWHzZujSpUi3Ih6jfJMrVVVPAacURVkDpKqqqgNQFEUDWDyi+IQQQogiSdkdhD4tDYfu3Y0+59z1RGYGhbPp9HVMTUzo41eZka08qO5832t4F7fDipfBwhZe3gKVfI2aPzEzkbWX1hIYEkhkSiTOVs6M9RlLv1r9cLF2eWB8WhpMmgTe3hAYCFWrGn0r4glgzJ6rbUAHIOXO11Z3jjUvraCEEEKI4kr66y805cph3bhxgeNUVeVgeCy/7Q5j78UYbC1MGdnSgxHPuVPe3vLBEw7PMTRgLu8Fg5aBQ+VCYwmODSYwNJBN4ZvI1GXi5+LH+IbjaV+tPWYmZg+Mv3wZKlcGa2vYtcuQVJk9OEw84YxJrixVVb2bWKGqaoqiKNalGJMQQghRLKpWS+q+fdh3eT7fSus6vcrWczeZFRTGqchEytlaMOn52rzYxBUHqzwyGV02bPsQDs2EWl2g7++Glat8ZOmy2HZlG4EhgZyKPoWVqRUvVH+BgNoB1C5bO9/zAgNh1Ch44w344gvw8Cjy7YsnhDHJVaqiKH6qqh4HUBSlIZBeumEJIYQQRZd2/AT6lJQ8N7JnaHWsORHF7D3hXI5JxdXJmi9616OvXxUszfJOxMhIhJUj4NLf0HQcdPocTPIeezP1JstDl7Pq4iriMuJwtXfl3Ubv0qNGD+zN7fONOTUVxo+HuXOhWTMYPbpYty6eIMYkV28BKxRFuX7n64rAwFKLSAghhCimlD1BYGaGTbN/d64kZWhZ/M9V/th/mejkTOpVtueXwX48X68CGpMCKnDGhcOSAIgLg+7Twf/lB4aoqsrhm4cJDAlk17Vd6FU9rau0ZpDnIJpWaoqJUnBphvPnDW8DhoTA++/Dp5/KY8CnQaHJlaqqRxRF8QRqAwoQoqqqttQjE0IIIYooJSgIa/+GaGxtuJ2Uwdz9l1nyz1WSM7NpUcOJHwb40qKGU6638vJ0eS8sN7S6YehacG+Z+zpZKWwI30BgSCDhieE4WjjyktdLDKg9gMq2he/Fukuvh/R0Q/2qjh2LeLPiiVVocnVnf9XbgKuqqiMVRampKEptVVU3ln54QgghhHGyIqPIuhSGvksP3l99mlXHosjW6+lSvyJjWlWnfhUH4yY6Og82vwNlq8PgQCj77+ansIQwloYsZUPYBtKy0/By8uLzFp/zvPvzWGiMe5E+Pt6wv2rsWKhXDy5ckNWqp40xjwXnYahz1ezO15EYqrNLciWEEOKJcWHdX2iAl4PNuH0jiv7+VRjZ0gO3cjbGTaDTwtYPDK1sanSEfnPB0oFsfTa7ru0iMCSQwzcPY25izvPuzxNQO4D6zvWLFOO+fTB4sKGFTZs2UKeOJFZPI2OSq+qqqg5UFGUQgKqq6Uqh66lCCCFE6VNVlb0XY5gZFEan5ZtwtS1Hj25NGf6cOy52eZRTyE9qDKwYDhF7odnr0HEqMZnxrDo1i+UXlnM77TYVbSoy3m88fWr2oaxl2SLFmZ0Nn38On30G7u6wf78hsRJPJ2OSqyxFUawAFUBRlOpAZqlGJYQQQhQgW6dny9mbzAwK49z1JKpYK/jHhePQry9tuxQxa7lxGgJfhJRbqL1mcbJSbZbuez+neXKzis34sMmHtK7SGk0+bwoWpndv2LgRhg2Dn38GO7tiTSP+I4xJrj4G/gKqKoqyGGgBDC/NoIQQQoi8ZGh1rDgWyZw94VyNS8PD2Yav+9anU0o4N5dkUqZdm6JNeHY1rHuNNKsybO78HsuurSLk1L/NkwfWHoibg1ux41VVUBR4+WUYNMjwSFA8/Qpq3NxCVdX9wB6gD9AUw9uC41VVjXlE8QkhhBAkpmlZdOgK8/ZfJiYlC9+qjnzQtQ6d6pbHxETh5tQFKJaWhVZlz6HXwc7PuPLPDJZVrslaC4Xk4HnUKlOLKc2m0M29W07z5OJISIBx46BJE0MNqz59ij2V+A8qaOXqJ6AhcFBVVT9g06MJSQghhDC4mZjB3H3hLDl0ldQsHa1rOTO2TXWauJfNKaegqiopQUHYNG2KiUXhb+zpUqPZu3IwgWmX2V+1EqZKBh0rdyLAM4AGLg0KL9NQiD17YOhQiIoCH5+Hmkr8RxWUXGkVRZkHVFEU5af7P1RV9c3SC0sIIcSz7NLtFGYFhbH2ZBR6Fbp7V2R0q+rUrfRgpfOs8HC0UVE4jXy1wDnjMuJYffwXVoQs57oGXOydGVdvGP1q9sPZ2vmhY87Kgo8/hq+/hurV4cABMHYhTTxdCkquumNo2NwOQykGIYQQolQdvxrPzN1hbDt/CwtTEwY1rsbIlh5ULZv/I7qUoD0A2ObR8gbgTPQZAkMD+St8E1mqjkZ6lf/zeZ22DV7Js3lycR07ZkisXnkFfvgBbPNvPyiecgUlVxNVVX1XUZRqqqoueGQRCSGEeKaoqsruC9HM3B3GoctxOFiZ8Wa7Ggxr7kY528If86UEBWFRsyZmlSrlHMvIzuCviL8IDAnkXOw5rBUNvRMTGGhXi5oBi8H24VeqwFBhff9+aNnS0Bfw7FmoW7dEphb/YQUlV10VRfkICAC+eUTxCCGEeEZk6/RsPH2DmUFhhNxMpqKDJR91q8OgxtWwsTDmZXbQpaSQduwYTsNfAiAyOZLlF5az5uIaEjIT8LCryvvZtvSIDMa26WvQ4VPQGDd3Ya5cgeHDISgITp2C+vUlsRIGBf0X9hcQA9goipJ0z3EFUFVVzb/FtxBCCJGP9Cwdy45cZc7ey0QlpFPDxZbv+vvQw6cS5qYFNzq+X+r+A5CdTVgdRz7d8Rp7I/diopjQtmpbAuxr0/jvr1F0Wug3H7x6lUj8qgoLFhjeAtTrYe5cQxsbIe7KN7lSVXUiMFFRlHWqqvZ8hDEJIYR4CsWnZrHgYAQLDkQQn6bF37UMn/bwop2nCyYmRX9DLzEzkVPr5mJracLYW9NxtCnHKO9R9KvRlwrHF8KG98ClDgxYCOVqlMg9qCoMHAgrVkCrVjB/vqHiuhD3KqjOlaeqqiGqqvZUFMVCVdXMez5rqqrqP48mRCGEEP9lUQnp/L43nMDD10jX6uhQx4Uxravj71a0FjJ3nY89T2BIIFvCNjHjnzQiPMvyVduP6FCtA2YZSbBmNFzaDt4DofsPYG5kb0EjKIphf1Xz5vDmm2BStIU28Ywo6LHgEsDvzu8P3vN7gF/v+1oIIYTIJfRmMrOCwlh/6joAPXwrMaZ1dWqVL3rvlyxdFlsjthIYGsjp6NNYmVrxsq4pDmk7qfvSR9i7d4Gr/8CKlyEtBrpNA/9XDNnQQ4qJgddfh759oX9/eOONh55SPOUKSq6UfH6f19dCCCEEAEci4pi5O4wdIbexNtcwrJkbr7R0p7KjVZHnup5yneWhy1l9cTXxmfG42bvxbqN36VGjB2nf/kyCuTk2zz0H+6bDjqngWA1e/Rsqlkz1zlWrYOxYQ8X1Fi1KZErxDCgouVLz+X1eXwshhHiG6fUqO0Nu81tQGMeuxFPWxpwJHWoxrJkrZWzMizaXquef6/+wNHQpeyINNazaVGlDgGcATSo2wUQxQVVVbm3fjk2zxmjWvwIXt0LdntBjBlg6PPT9REcbVquWLwc/P9ixw/A2oBDGKCi5uluZXSF3lXYFqFzqkQkhhHjiZWXrWX/qOrOCwrh4O4XKjlZ82sOLAf5VsTLXFGmuxMxE1l1ax/ILy7mSdIWylmV5pd4r9K/Vn4q2FXONzThzhuybN7HzvAnhMdDlW2g8skQeAwLs3g1r1sDnn8OkSWBWcrVGxTOgwCKi9/z+6H2f3f+1EEKIZ0hqZjZLD19l7r7L3EjMwLOCHdMH+tLNuyJmmqLt8g6JCyEwJJBN4ZvI0GXg6+zL2JZj6ejaEXNNHqteeh3Jcz8HRcXO3QKGbIXKD78N+OZNOHIEXngB+vWDRo3Aze2hpxXPoIJKMUhVdiGEELnEpmSy4EAECw5eITFdS2P3snzZpz5tajkXqeFxli6LbVe2ERgSyKnoU1iZWtHNoxsBngF4lvXM/8SkG6irXiVp/wVsqpdDM34rWD5c2UVVhYUL4a23DL+/ehXs7CSxEsVXMmVqhRBCPNWuxaUxZ284y49eI0Orp1Pd8oxpUx2/amWKNM+NlBusuLCCVRdXEZcRh6u9K5MaTaJnjZ7YmxeSJF3YBmvHkHk7E22KPU4vvvnQidW1azB6NGzZYiivMHeuIbES4mFIciWEECJf568nMWtPGBtP38BEgV6+lRnd2oMaLsZnIHpVzz83/iEwJJCgyCAAWldpTYBnAE0rNsVEKeQxojYDtk+Bw7OgfD2S6QxKIHYdOjzMrREba9ikrtXCjz/Ca6+BpmjbxITIU6HJlaIoLVRV3V/YMSGEEE8HVVX5JzyOmUFhBF2IxsZcw4gWbox4zp2KDsaXU0jMTGR92HqWhS7L2aA+ot4I+tfqTyXbf5ss61JSMbGxzvux4q3zsOoVuH0emoyFDp+Q3C8AKz8/TJ2L13z59m1wcQEnJ/jf/6BzZ6myLkqWMStXM3iwYGhex4QQQvyH6fUq287f5LegcE5dS8DJxpx3OtViaFM3HKyNf13u/g3qPs4+jGk5hk6unR7YoJ4RGkpE/wHYtGhBxamf/pswqSocng3bJhtKK7y4Cmp2IOvKFTJDQ3F5790i319mpiGZ+t//DKUVmjeHMWOKPI0QhSqo/U0zoDngrCjK2/d8ZA/IwqkQQjwlMrN1rD0RxaygcMJjUqlW1prPetWjf8MqWJoV/se9qtORmZnG3zeDcjaoW2os6erRlYDaAdRxqpPvudE/zQBTU1IPHCD8hR5U+PRT7Jv7wPrX4eI2qNkJev4KtoakK3n7dgDsO3Ys0j0eOAAjR8L58zBoENQomVaDQuSpoJUrc8D2zph7H64nAf1KMyghhBClLzlDy5JDhnIKt5Mz8apkz4xBDehSrwKm+ZRT0CUkkLxjJ1kRl8m8fJm0sEtkX7tGqrnKlyNNKFvBjUmNJtGjeg8cLAou5pl+5gwpO3bgPP5N7Dp14vq77xE1fjzJ1XVUaJiEptuDtauStm/H0ssLs8rGl1v8v/+D77+HqlVh40bo1s3oU4UoloJKMQQBQYqizFdV9cojjEkIIUQpik7OZN7+yyz85wrJGdm0qOHEtAE+PFejXIHlFFStlisvjyAzOBjVVEN8OUsu2acR00Dh+WPwy7U2+Iz5qfAN6nfj+PEnNI6OlBk6DI2pDrdB5YhZmkTMeXvSkmpQrp4j9l6paGxtAdDevEnGqdM4v/VWoXOrd/qIKApUrgwTJsDUqXBnKiFKlTF7riwURZkNuN07XlXVdqUVlBBCiJIXEZPK7L3hrDwWiVanp2u9ioxu7YF3FUejzo/69Scyg4NZNNCFjW6xOFrb0LfmMAbX6g/fzSJh5Sq0I69gYcTu8LRjx0jdtw+Xie+giT0Na0ahJEbiPPZtbMu9wI1PpnJzysfc+vIr7Dp1xLFPHzIvXATArlOngu8zAsaNg2HDICAA3n67wOFClDhjkqsVwEzgd0BXuuEIIYQoaWejEvktKIwtZ25gamJC34ZVGNXKA/dyNkadfz72PFu3zaTDrO0c8FK40qQqX3hOzLVBPfv110lav4Ho77+nyowZBc6nqirR039E4+REmfKXYN7/QRlXeHkLVGuKFeC+ehUZp0+TsHoNSZs3k7R+AygK5jWqY+GRd/KWlQXTpsFnnxlKKvSTDSziMTEmucpWVfW3Uo9ECCFEiVFVlf2XYpkZFMa+SzHYWZgyqlV1RrRww8XestDzM3WZbIvYRmBoIOdunuKb+SpaO0taTZvNaLdGD4w3LVcOp1GjiJ4+nbQjR7Bu9OCYu9L++Ye0I0co38oSk6O/QsPh0OlzsPh3e6+iKFj5+GDl40P5998jefvfJG3ejH3XLnnOuX+/YcN6cDD06QPTpxv2WAnxOBiTXG1QFGUcsAbIvHtQVdW4UotKCCFEkWhv3+bm1Kk4v/c+O+JMmBkUxpmoRJztLHiviyeDm1TD3rLwcgqRyZGsuLCCNRfXEJ8Zj5u9G9+HN6Xi7f1U+e177PJIrO4q+9Iw4gMDufXNt7gtC0QxeXDvlarNJHrqJEytdTjWyIDehhILBTGxtMThhe44vNA93zE3bkB6umxYF08GY5Krl+78895GzirgUfLhCCGEKI6Y5StJ+XsHOy4n80n9AbiXs+F/ferT268yFqYFl1PQq3r2R+1nWegy9kTuQVEU2lZty8DaA/GJteXKR4Nw6N0bu7ZtC5zHxMoKlwlvcf3d90jatPnBZOjmGVJ+eIX0y8lU6O2JyRt/gpVjse5Xp4PZsyE7G954A/r2he7dwbLwRTkhSp2i3n2l4gng7++vHj169HGHIYQQ/xmJ6VoWHYyg9vujqJB4CwW48d1s2nZtgcak4EbKCRkJrL20lmWhy4hMicTJ0om+tfrSv1Z/KthUQJ+VxeU+fdAnp+CxYT0a+8L7+Kl6PRH9+pOdEE/1zZsxsbSE7CzYOw016Dsub3dGb+pE9b93o5gZX5j0XocOGTasHz8OL7wA69blqtYgxCOjKMoxVVX97z9uTPsba+BtoJqqqqMURakJ1FZVdWMpxCmEEMIIt5Iy+GPfZRYfuorz7av8mniT9GEjsVm7jFobFqLp/ly+556NOUtgSCB/RfxFpi4TPxc/3vR7kw7VOmCm+Tfhifn1V7IuhVF1zmyjEisAxcQEl0mTuDp8OHHzF2Dj6Uzaos9JDYsnPbYi+iwdFf83sViJVUwMvP8+/P47VKoEgYEwYIAkVuLJY8xjwXnAMQzV2gEiMbxBKMmVEEI8YuHRKczeE87q41Fk6/V0867EGOUYmJriPfZlEpztiJ72/QObyjOyM/gr4i8CQwI5F3sOK1MrelbvyUDPgdQqU+uB66g6HQnLV2DXsSO2LVsWKUabpk2wbdOa6OnTib5zzLxyVez7tMO2ZUts2xWvks/ly/Dnn/DOOzBlCtgZ3ztaiEfKmOSquqqqAxVFGQSgqmq6UlCVOSGEECXu5LUEZu4OY+v5m5hrTBjQqAqjWlanahlLLnX4PyyaN8O0TBnKDhlC/MJF3J72Pa5LlxCZHMmy0GWsubSGpKwkPBw8+KDJB7zg8QK25vlX1Ew/eRJdXFy+b+cV6OohKlQ7SlztFCwbNMb65a8wc30wgTPGvn2GX++9B40awZUrUKFCsaYS4pExJrnKUhTFCsMmdhRFqc49bw0KIYQoHaqqEnQhmplBYfwTHoe9pSmvtanB8BZulLO1ACDt+HGyr9/A5U7VchMrK8q+No7bH3/Ctz/0Z6FTKBpFQ7tq7RjkOQj/8v4FVmG/K/nvHWBmhk1RVq0yk2HHVDg8BzOHKpT/bl6hbwLm5/p1mDQJFi8GV1d4/XVDdXVJrMR/gTHJ1cfAX0BVRVEWAy2A4aUZlBBCPMuydXo2nbnBzKBwgm8kUd7egg+71mFQk2rYWuT+Yztp40YUS0ts27UnNj2WNZfWsIrlvFMW/NeEYvvjaPrW7k95m/JGX19VVZJ37MCmadOc1jOFurANNk6ApChoMhraTQaLoveaycgw1Kj64gvQauGjjwyrVjbG1TsV4olQaHKlqup2RVGOA00BBRivqmpMqUcmhBDPmPQsHSuOXWPO3nCuxaVT3dmGb/p508u3MuamedWM0pK05S+ym/ny4YnP2BaxDa1eS+MKjTEf24FyX8zDL6Iyjn7GJ1YAWZcuob16FacRLxc+OPkWbH0fzq4C5zrwynaomn8trMLExBgqrHfsaKi2Xr16sacS4rExZuUKoDKguTO+laIoqKq62pgTFUXRAEeBKFVV868AJ4QQz6iEtCz+PHiF+QciiEvNwq+aI5O71aVDnfKY5FNOIU2bxp6VP+IaH8+0socJuWZPv1r9CKgdgIejB6qqErH+KNEzZmDfrSsmFhZGx5O8YycAtm0L2Hiu18Px+bD9E8hOhzYfwHMTwNS8CHducPYsLFoEX30FVarA+fOGR4FC/FcZU4rhD8AbOAfo7xxWAaOSK2A8EAwY9x6vEEI8I64npDN332WWHr5KWpaOtrWdGdO6Oo3dy+a7Lyo8MZzloctZf2k9w1Yn4GJlQrfBH/FLrZ5Ym1nnjFMUBZf/e5urw18mdtZsnN98w+i4knfswNLbG7PyLnkPuHUeNr4F1w6BW0voPh3K1SjCnRtER8PHH8OsWWBvD6NHg7u7JFbiv8+YlaumqqrWLc7kiqJUAboBX2ColSWEEM+8i7eSmRkUzrqTUahAD59KjG7tgWeFvP8OqtVr2X1tN8tClnHo5iFMTUx5vmI7WoXtxLFrN/y8BuV5nk3Tpjj07EnMzJnYtHwO6wYNCo1Ne+s2GWfO4Hxng3wuWakQ9A0c/Bks7KHXTPAJKHKhqawsmDHD8PgvJQXGjoVPPoFy5Yo0jRBPLGOSq4OKotRVVfV8MeafDkwCpBqJEOKZd+xKHL/tDufv4FtYmpkwpKkrr7Z0p0oZ6zzH3067zaqLq1gZupLb6bepaFOR8X7j6V2jN2ZBR4hK34xD94Ib6ZWf/BFpR49yfdK7uK9Zg8a24J3hKbsMjwTtOrT/96CqQsgm+Os9SLwGvi9Cx8/Axqlo34A7srLg+++heXP47juoW6y/vgvx5DImuVqAIcG6iaEEgwKoqqp6F3SSoijdgduqqh5TFKVNAeNGAaMAqlWrZmTYQgjx36CqKrtCb/Pb7jCORMTjaG3G+PY1eam5G2VtHtyfpKoqR24eITA0kF1Xd5GtZtO8UnM+avoRraq0QmNi6BMYuWkTGudyWDduXOD1Nba2VPrma64MHcatL7+k0pdfFDg++e8dmLlWw/zuTvK4y7DlXbi4FVzqwst/gWuzIn8fjh6Fn34yVFe3tYVjx6Ssgnh6GZNc/QEMBc7w754rY7QAeiiK0hWwBOwVRVmkquqQewepqjobmA2G3oJFmF8IIZ5YWp2eDaeuMysonNBbyVRysOTjF+oysFFVrM0f/KM3OSuZ9WHrWR66nPDEcBwsHBhSdwj9a/Wnmn3uv3jqkpJI2R2E46AAFE3BTZkBrBs2xGnUSGJnzsK2dWvsO3fKc5wuJYXUQ4coO2QISnYmHPgJ9k4DE1Po9IWhxIKmaG1rrl6FDz4w1KtydoaQEPD2lsRKPN2MSa6uqqq6vqgTq6r6PvA+wJ2Vq3fuT6yEEOJpk5aVzbIj1/h972WiEtKpXd6O7wf48IJPJcw0D5ZTCI0LJTA0kE3hm0jPTqeeUz0+b/E5nd06Y2lqmec1EjduRNVqcehW8CPBezm/9hqp+/Zzc8oUrHx9MCv/YHmG1D17QKvFrqY1/NoE4iOgbi/o/CU4VDb6WgCZmYZ9VD/8YNiS9cEH8O67ho3rQjztjEmuQhRFWQJs4J7K7MaWYhBCiGdBXGoWCw5E8OfBCOLTtDRyK8NnvbxoW9vlgTf/snRZbLuyjeWhyzlx+wQWGgu6undlYO2BeJXzKvA6alYWsXN+x8rXF0vvAndn5KKYmVHp22+43KcvN95/n6q//45ikjvZS96yHo21BquTH0J5Txi2DjzaGH0NMGzPUhQwM4Pt2w2NlT//HGTXh3iWGJNcWWFIqu5dRy5KKQZUVd0N7C5KYEII8V8QGZ/G73svs+zINdK1OjrUKc+Y1h74u5V9YGxUShQrQlew5tIa4jLiqGZXjYn+E+lZoycOFg5GXS9h9Rqyb9yg4tSpRrWxuZeFuzvl33uPmx9/zPV3JuLyzv9hVqkSZCaj7vqGlN27sHPVoXT5EhqPKtIjQL0eli+Hb7+FbdvAycnQE9Ay78U3IZ5qxiRXv6uquv/eA4qitCileIQQ4j8h5GYSs4LCWX/qOgrQ07cyY1p7ULN87pej9aqe/VH7WRa6jD2Re1AUhTZV2jDQcyBNKzbFRHnwUWF+1KwsYmbPwtLHG5vnivfHsOOA/mTfukns73NJ/vtvynT0w6nMfjIiE9BrnbAb8xU061OkOXfuNDzyO3rUsJ/q5k1DciWJlXhWGZNczQD8jDgmhBBPNVVVOXw5jplBYewKjcbaXMPw5m688pw7lRytco2Nz4hn7aW1LA9dTmRKJE6WToz0Hkn/Wv2pYFO83dwJa9eSff0GFT/5pMirVncpioLzm2/i2Lw60V99RNymgyRYmGJW1Q/F8hY27bsYPVdmJvToYVipqloVFiyAF18EI/bYC/FUyze5UhSlGdAccFYU5d4CoPYYWuEIIcQzQa9X2R58i5lBYZy4moCTjTn/17EWQ5u54mj9bzkFVVU5E3OGZaHL+OvyX2Tps2hYviHj/cbTvlp7zIr4pt29VK2W2FmzsaxfH5uWLYt/MwlX4e9PMDu7ikrNK1H25deI3hRMyq5d2HXsiImVVaFTxMYaVqYsLAxJ1bRpMG6crFQJcVdBK1fmgO2dMfeucycB/UozKCGEeBJkZetZezKKWUFhhEWnUrWsFZ/19KJfw6pYmf/7d8z07HS2XN5CYEggwXHBWJta07tmbwbWHkjNMjVLJJbEdevQRkVRfvJHxVu1ykiEvd/DP7+BYgKtJsFzb2FpbkPV7pAREoJpHm8Q3isqyrA5ff58OHUKatUy1K0SQuSWb3KlqmoQEKQoynxVVa88wpiEEOKxSsnMZumhq8zdd5mbSRnUqWjPjwG+dKtfEdN7yilcTrzM8tDlrLu0jmRtMjUca/BRk4/oXr07NmYFV0IvClWrJWbmLCzr1cO2deuinazLhmPzYPdXkBYLPoOg3eQHSitYenrmO0VMDHz9Nfz8M2Rnw6hR4GDc/nshnknG7LlKUxTlW8ALQzFQAFRVLaBduhBC/PfEpGQyf7+hnEJSRjbNPJz4up83rWqWy1ktytZns/vabgJDAzl0w9Dnr6NrRwJqB9DApUGx90IVJHH9BrSRkZT/8APj51dVCN0Cf38CMaHg+hx0/hwqFd5f8F5paYb2NLGxMGSIodGyh0fR70GIZ4kxydViYBnQHRgDvAREl2ZQQgjxKF2NTWP23jBWHI0kS6enc90KjGlTHd+qjjljbqfdZtWFVay88G+fvzfrj6N3nf6Usyq9jsNqdjYxs2Zh6eWFbZs2xp107QhsnwxXD4JTDQhYArW7Gt1gOS0N1q2DQYPA2hq++QYaN5YegEIYy5jkyklV1bmKooy/51FhUGkHJoQQpe3c9URmBoWz6fR1TE1M6ONXmZGtPKjubAvceTvw5mGWhS5j59Wd6FQdLSq3YHLtyTROq8C1wUNQX1ENu7lLSeL6DWivXqX8r78UvmoVcwl2fArB68HGBbp9D37DjK5XlZEBs2bBV1/BrVtQpw74+sLw4Q99G0I8U4xJrrR3/nlDUZRuwHWgSumFJIQQpUdVVQ6GxfJbUBh7L8Zga2HKyJYejHjOnfL2hp0PSVlJrL+0nmWhy4hIijD0+aszhAG1B1DNvhqqqnL15RHoU1OJ+WkG5lWr4vDCCyUfa1YWMb/8gmXduti2bZv/wKTrEPQNnFgIppbQ5gNo9hpY2Bp1naws+OMPw2b1qCho2xZWrTIkVkKIojMmufpcURQH4P8w1LeyByaUalRCCFHCdHqVbeduMjMojFORiZSztWDS87V5sYkrDlaGlZ3zsedZHrqczZc3k56djnc5b7547gs6uXbK1ecv+e+/SfvnH1zefZeUnTu58cGHmFWsiLW/f4nGHL9yJdqoKCp88nHeq1ZpcbDvezg8B/Q68B8BrSaCrUuRrpOVBR99BLVrw59/QjvZUSvEQ1FUVX3cMeTw9/dXjx49+rjDEEI8RTKzdaw+HsXsPeFcjknF1cmaUa086OtXBUszDZm6TLZGbGVZyDJOx5zGytSKru5dGVB7AHWdHtxkpM/MJLxbd0ysLHFfswZ9SgoRAYPQJSTgtiwQc1fXEolbn55OWKfOmLlWw3XhwtzJVWYy/DMTDvxk+L1PALR5D8q4GTW3Vmsop7ByJWzebCj6efWqoWZVKezHF+KppSjKMVVVH/hbVaErV4qi1AJ+A8qrqlpPURRvoIeqqp+XQpxCCFEikjK0LDl0lT/2XeZ2cib1Kzvwy2A/nq9XAY2JwrWka6w4Zejzl5CZgJu9G+81fo8Xqr+Avbl9vvPGzV+ANjKSavP+QDE1RePoSNVZM4kYMJBro8fgFrgUjaPjQ8cfv2QJ2dHRVP7h+38Tq6w0OPI77J9uKKtQuxu0+wjKG7fTPCvLUEX9yy8hIsKwSf3WLahUSRorC1GSjHksOAeYCMwCUFX1tKIoSwBJroQQT5zbSRn8sT+Cxf9cITkzm+dqlOOHgb40r+6EXtWzJ3I3yy4sY3/UfjSKhnbV2jGw9kAaV2hc6IZx7a3bxMyahW2H9tg0a5Zz3NzVlSq//MzVl0cQ+eZ4qv0+B8XcvICZCqZLSSF29hxsWrY0PGrUZsCx+YZHgCm3oHo7aPshVDH+MWRYmGEv1bVrhqTql1+gSxdZqRKiNBiTXFmrqnr4vj90skspHiGEKJbLManM3hPGqmNRZOv1dKlXkTGtq1O/igMx6THMOTOHlRdWciP1Bi5WLozzGUefmn0ob1NwVfJ7RX8/DbRayk+a9MBn1v7+VPzic65PepfYP/6g3Jgxxb6XuHnz0SUm4vz6WMNK1d7vISnKUKuq/wJwbVb4JEB6Opw/Dw0bgpsbtGoFQ4dCp06SVAlRmoxJrmIURakOqACKovQDbpRqVEIIYaTTkQnMDApjy9mbmGlM6OdfhVEtPXB1suboraNMDFrO31f+JlvNpknFJkxqNInWVVtjZlK0Pn/pJ0+SuG49TqNGYZ7PMzSHHj1IWLWaxDVrcRo9ulgFRbPj44mbPx+7RjWx2vGiIamq2gR6/QrurY3KilJSYOZM+O47Q0X1q1cN9aoWLSpyOEKIYjAmuXoNmA14KooSBVwGXizVqIQQogCqqrL3Ygwzg8I4EBaLnaUpY1tXZ3gLN6wstGwIW8db+5YTlhiGnbkdg+oMYkCtAbg5uBXveno9N7/8ClNnZ5xGjSpwrH33btycPIWMs+ewql+vaBfSZhA79Q30aak4l9sPDv7Q8xfwaGNUUpWQYGhR88MPEBcHHTvChx+CEb2YhRAlqMDkSlEUDTBWVdUOiqLYACaqqiY/mtCEECK3bJ2eLWcN5RTOXU/Cxc6C97t4MrhJNaLSwvj1zP9yyih4OXkxtflUnnd/HivTh8su0g4fJuP0aSp+8QUa24J7Btp36sTNqZ+RtHGj8clVViocnYd2+wzityk41LXF4o1VRq9U3XXmDEyeDC+8YEiqmjQx+lQhRAnKN7lSFMVUVdVsRVEaAqiqmvrowhJCiH9laHWsOBbJnD3hXI1Lw8PZhq/71qeLdzmCIncwZucUTkefxkJjQVf3rgysPRCvcl4ldv20I0fBxAS7zp0KHatxcMC2VSuSNm/GZdJEFI2mgBtLNNSo+udXSIsl9lIdVCWFctPXGOoiFCIiwvDoz9LS8M+WLSE0FGrVKsLNCSFKXEErV4cBP+CEoijrgRVAToKlqurqUo5NCPGMS0zTsujQFebtv0xMSha+VR35oGsd6lTNYtXFlXRbYyij4GrvykT/ifSs0RPr+HRifvqNqJRk7Lt2xbZVq4d6cw8g7fgxLGrXRmNrXMVzh+7dSNmxg7QjR7FpmsfyUUo0HJppSKwyE6FmJ9Rmb5E0+B3sOnbEvJDE6tw5Q7+/xYvBxATufVIpiZUQj58xe67KArFAOwyb2pU7/5TkSghRKm4mZjB3XzhLDl0lNUtHm9rOjGrlRobZWZZf+ISJxwxlFNpWbcuA2gNoUrEJamo6sb/N4ca8+aDXY2JnR9LmLWgcHLDr2gWHF3pg1cC3yJvMVa2W9FOncezd2+hzbNu0wcTamqRNG3MnV/ERcOBnQ5ua7Eyo0x1avgOVfEnZtQtdQgIOPXoUOPeMGfDmm4YN6m+8Af/3f1BFGpIJ8UQpKLlyURTlbeAs/yZVdz05Zd2FEE+NS7eTmRUUztqTUehV6O5dkYFNHDmbvJ2PT3zIzdSbuFi5MNZnLH1r9qW8TXnU7GwSli0nesbP6GJjse/WDecJEzAr70LqgQMkrltP4uo1JCwNxKJOHSp/Pw0Ld3ejY8oICUVNS8O6oZ/R55hYWWHXsQNJW7dRfvJkTOIuGAp/nl0NiomhonqL8VCuZs45SRs2oHF0xPa553LNpdfDxo2GUgre3tC5M3z6Kbz2Gjg5GR2SEOIRKii50gC25E6q7pLkSghRYo5fjWfm7jC2nb+FpZkJgxpVpWndRHbeWMxre3fkKqPQpmqbnDIKqk7HleHDST96DCv/hpT/7VesvL1z5rVt3Rrb1q3RpaSQ/Ndf3P5uGhH9B1Dxqy+x79jRqNjSjx8DwMrP+OQKwL5bNxLXrSd1alfsTI+AuS00HWtoqGxfKddYXUoKyTt24ti3L4qZ4d4yMgyP/aZNg+Bgw6O/WbMMj/2mTClSKEKIR6yg5OqGqqpTH1kkQohniqqq7A6N5regMA5fjsPByowxbSriVOEMmyJmseZgGPbm9gWWUYhfGkj60WNU+HgKjgEB+T7y09ja4tivHzbNmhH51gSi3niTjFdfwfmtt1BMC94dkXbsOGaVK2NWoYJxN6bTwtnV2Jz+CY2FjsRj17D7YAo0fBmsy+Z5SvK27aiZmTj0eAEwlFP4/HNDaxpfX0OSNWCAcZcXQjx+Bf2pIvV7hRAlLlunZ+PpG8wMCiPkZjIVHSwZ3cGCVIu9rL66hfRb6dRzqldoGYXsmBiif/wRm+bNCkys7mVWuTKuixdx68svif19LumnTlP5+2mYOjvnOV5VVdJOHM/V6iZfaXFwfIFhk3pSFIqzJ/atGpOw5yy6BmPQWOdfwiFxw3puOjeiVn0fAG7ehAYN4J13oF07qaYuxH9NQclV+0cWhRDiqZeepWP50WvM2RtOZHw6NVwsGNohmrDM7SyJOo2lxpIu7l2MLqNw+9tv0WdkUP6jyUXapG5ibk7FTz7ByteXm598SsTAADy2bMbEwuKBsdpr19BFx2Dt1zD/CWMuwj+/wamloE0z1KbqPh1qdMD+5Enit79Iyo6/cejZ84FTVRX2bIzj85WD2ZHagXWbFV54AaZONbwFKIT4b8o3uVJVNe5RBiKEeDrFp2bx58ErLDgYQVxqFvXdtDTwPc2JuG2sjUrEzd6NSY0m0aN6DxwsHIyaM/XwYUMrmjGjsfAwfnP6vRx79UJj70DkuHGk7NqF/fPPPzAm7dhxgAc3s+v1EL4TDs2Ci9tAYwHe/aHJWKjwb+FQK19fzCpVInHjplzJlU4HK1fC99/D4cNlsTdpxKSxifj7OwKSWAnxX2dMKQYhhCiyqIR0ft8bTuDha6Rrs/DzvEGtsoc4F3+EyFumtK3WloG1B9K4QuMirTypWi23PvsMs0qVKDd69EPFaNu6FaYVKpCwenWeyVX68WOY2NtjXr264UBGkmGF6vBsiL0ENi7Q5n3wfwVsH3y0qJiYYN+tG7F//EF2XBzYl+XuFq/33gNTU/jUexZ9a+zH65c/H+pehBBPDkmuhBAlKvRmMrOCwlh/6jpokvDyDCbRdB8XM27jkunCON9x9K3ZFxdrl2LNH/fnQjIvXqLKr79g8pBN8xSNBoeePYmdMwftrduYlc8dU9qx41g3aIASewmOzIGTSyErGSr7Q585ULcXmBZcoNS+e3cO/7yDb/reZndEWYKDDRXVd+0Cl/QLXOk9nfK9P3qo+xBCPFkkuRJClIgjEXHM3B3GjpBbWNtfpka9k9zIPkZ4to7mLs0ZUPsDWldpjalJ8f/Y0d68SfQvv2Dbti127dqVSNwOvXoSO2sWSRvW4/TqqznHs2NukxUejkPVBPilEZiYQb0+0Hg0VClgD9Ydej1s3gw//VSL7RGbML+SSb/G50hO9MTSUoObG9yetgE0Guy7dimRexFCPBkkuRJCFJter7Ij5DYzg8I4di0KB+eTVKl/lMTs66SYODC0zhD61+6Pq71roXPpEhJIP3OG9JOnSD99mozgYEzMzdE4OaEpWwbTsk5khoeBTkf5Dz8osXuwcHfHys+PhNVrKPvKKyhJ1+H4n6Svng+YYO2QCO2nQIOhYGv8atvBg4YGypUqwWdTsugR+w1mfy8h7f/80U77DlNnZxI3bsL2uecwLZt3iQYhxH+TJFdCiCLLytaz/tR1ZgWFEZYUjGP5o5TxPEG2moVrGW8G1n6NTq6dsDS1zHWe9uZNYn+fiy4hAX1aWs4vXXw82mvXDIMUBYsaNQyVylU92bFx6KJjyAy9gC4hAefx4zEv4X4vDr16cHPKJ2R82wOr9H2gqqSl1EMxTcDy08NgZV3oHGfPwi+/gIMD/O9/0Lw5bNhgqKhuZmYOTCZhbX1uTv2Myz174TgogOwbN7B/5/9K9F6EEI+fJFdCCKOlZmaz9PBVft8fSqx6CDuXI9g4XcXU1IpuHj0YWHsgnmU98zxXn5lJ5LjXyLx4EdNKFTGxtsHE2hqNgwNmlSvh2K8fVj7eWNarZ3SD5IeWGAknFmEfvoBbGj0JBy5g9cYE8BtG+tj3sfR2xaSAxEqrhXXrDEU/g4LAwuLfJsqKAt275x7v2KsXVt4+RL39NrG/zcTE2rrEHm8KIZ4cklwJIQoVm5LJggMRzD96mEyrfVhWOIGlko6rYw0G1v6Q7h7dsTUvOCG6+dlnZJw/T5Vff3m8CUV2FoRuhuN/QthOQEVTvR12z1mRdPwS5VtMAiD93Dmchr9U4FTvvWcop+DmBt98AyNGFN7vz8LDHbdlgcT88ium5V0eelO+EOLJI8mVECJf1+LSmL3nAitD/wK7g5hWDsdaMaOTW0cG1h5IA5cGRpVRiF+xgsSVq3AaM/rxJVa3g+HEIkMphbRYsK8Mrd6BBkOgjBuONQ6SFDSC5B07MHNxAa02Vz9BVTW84ffbb4bK6U2awMiR0KYNdO0KGo3xoZhYWODy9oSSv0chxBNBkishxAPOX0/ix6BDBN3YiKnDYUwrpFDeqhKD6rxFrxq9cLIqZHnmHulnznJr6mfYNG+O8xtvlGLUeUiLg7Or4ORiuH7C8MZf7S7gNwyqtwOTfzMi6yZNMK1UkcQ1a7Fu1MhwrEED4uJgwQJD0+TQUChbFvr1MyRXnp6GX0IIcS9JroQQgKGP3sHwaL4J2kBo2lY0tiGYOUGT8i14qd5gWlRugYlStNLh2fHxRI5/E41zOSpN+w6lKMs7xaXTwqUdcGoJhG4BXRZUqA/P/w/q9webcnmeppiY4NirFzG/zSQ7LhaLmjXAzpH61eD6dWjWDP78E/r3N9SpEkKI/EhyJcQzTq9XWX0qlBlHlhCjBGFiHoedgwP9ar3MEK+BVLKtVKx5VZ2O6+9MRBcdg+uSJZiWKVPCkd97MRWijsHpZYaVqrRYsHaCRq+CzyCo6G3UNPq2vVn4RTL/bG/Kwrf2otHATz9BzZrgbdwUQgghyZUQz6oMbTYz9m9jWehyMsxPoljocLWqxxi/d+ni3hEzjdlDzR83fz6p+/dT4bOpWNWvV/gJxREbBmdWwpnlhnY0Ggvw7AreA6FGBzDiHlQV9u2D2bNh5cqqZGR8iJfFWVKrNwWgb9/SCV0I8fSS5EqIZ8zN5Hg+D1rEnpvrUc1uolhY0dz5Bd5p9hK1ytYokWtkx8UR8+tv2LZpQ5n+/UtkzhzJN+HsajizAq4bGivj+hy0GA91e4Klcc2f79q82VAywd4eXn4ZAjz34jLnNdw7by3ZuIUQzwxJroR4Rhy4dopvD87jUupeMMnCSuNKH493eKNJP2zMbUr0WjG//Io+IwOXie+UzISpsRCywZBURewFVQ8VfaDjZ4aWNA7GFRXV6WDrVvj9d/D3hw8+gE6dDBvW+/YFGxuAluhe3o/Gzq5kYhdCPHMkuRLiKZaRncHis+uZf3YpCbpLqHozKmiaMq7hUPp4NSuVa2aGXyZ+2TIcB/THonr14k+UHg8hmwwJVfhuUHVQtjq0fMewMd25ltFTXboE8+cbfkVFgbOzYYM6gJkZDBuWe7wkVkKIhyHJlRBPoYjECH49tojt1zaSTSr6TBfq2Q/lw1ZD8K5UvA3qxrr9/TRMLCxwfv31op+cFmdIqM6vMyRUei04ukKLN8GrN1TwNpQ+N0JGxr9v9U2aZKik3qkT/PijoeefuXnRwxNCCGNIciXEU0Kr17Lz6k7mnFxMaOIJVNUEJbU+Har05r02XSnvUPqVwNOOHiXl7x04vzUe08JKld+VchtCNsL59XB5j2GFytEVmo4xJFSV/IxOqFQV9u41POZbsQKOH4caNQy9/mbMgMqVH+LmhBDCSJJcCfEfdzP1JstDVxAYvJLk7Dj0WkfM07oyxKs/I1v4YG/5cG/9GUvV67n19TeYli9P2ZcKbhtD3GVDQhW8Ea4dAlTDI7/n3oI6PQz7qYxMqADi4gzJ04IFcPky2NrCgAFgcqcsVy3jnyAKIcRDk+RKiP8gvarnwPUDLA1ext6oPaiqSnZqLcrq+jOuSTf6+lXD0uzhC3ZmhoeTfvIU2qioXL805cpRJiAA+65dMLnz7C1pyxYyzpyh4ldfPdgvT1UNFdJDtxge+90+ZzheoT60eR/qdAeXukVKqBIT4cYNQ4V0VTWsTrVoAVOnQu/edzenCyHEo6eoqvq4Y8jh7++vHj169HGHIcQTKy4jjjUX17AsdAU3UqNAZ0tmvD81LDvwRqvGdPKqgMbE+AQlP7qkJKJ//In4pUtBrwdFwbR8ecwqV8asUiUygs+TdSkMjaMjjv374dCnD9deHYmJvT3uK1cYKrFrMwxv9oVuNiRVyTdAMYGqTQ3JlGc3KONWpLi0Wti2DRYuNOyh8vOD/fvvfG/iDK1phBDiUVEU5Ziqqv73H5eVKyGecKqqcvz2cZaFLuPvK3+j1WtR0z3IiBtEk/Ktea1nbZpVdzKqgbIx10rasIFb33yLLi6OMgEBlB02FLNKlVDu2QGuqipphw4Tv3gxsX/MI3bO7wBUe/8tlJML4cI2CN8F2jQws4Ea7aB2N6jZCWyM70t4rxkz4PPP4fZtcHKCV16BoUP//VwSKyHEk0KSKyGeUMlZyWwI28CKCyu4lHAJU6zJiG9MVnwTunr6MvoFD+pVLlrBzLuyo6PRpaQYnqfp9ah6PfrkZKKn/0jakSNYentTdebM/2/vvuOrru4/jr/Ovdl774QM9t57KSDgAmW4tWoFbPlp1V+lWrW2aqsdSFv9CThqW7WSsITiaFEJS/YQEBOy9w7Z447z++MbBQQkkGnyeT4e93GTe7/3+z33HkLeOd/z/RxcBw644OuVUriPHYP76JFYDn1C+TtvQXk67jvvMDbwjoKht0OvmRAzGRwvfzG+1FR4911YutQITm5uMHky3HknzJ4tV/sJITovOS0oRCdzovQECUkJfJj+IXXWOjyIoSR/OKaaYSwcEccDk2KJ8ne77P1qu53q7dspf+ddanbuvOA2Jm9vgh59FJ8F81GmiyzSXFUIqZ/Cqf9C6mdQfxqUGSJHQ++ZRqAK6ndZ86e+UVAAa9bAe+/Bvn3GLjZsgDlzLntXQgjR5uS0oBCdWJ21jo/TPyY+KZ7jpcdxNDnjYRlFSdZQFNEsHhfNjyZEE+DhfNn7tlVVUbF+PWXvvYclMwuHoCACli7FqUcPMCkjRCkFJhNuo0adv8CypR6yvjACVernUHjceNwjGPpeD72mQ+xUcL2yhZm1Ng6fnw8REcYUr6FD4aWX4PbbjceEEOKHRMKVEB0o7XQaCckJfJD6AVWNVQS5ROFTu4Ds7P44efjyxNUx3DYmCg/ny/9R1TYb5e++R/GKFdhra3EdNoyghx/Gc8YMlOP3lGew240AlbbNuGXuBmsdmBwhaixMfxbirr6sgp7fVVMDmzfD++8bV/W9+y6EhsJf/gJXXQX9+1/RboUQolOQcCVEO7PYLHya9SnxyfHsL9iPg8mBXu7jyS4YQmphGHGBHvz+5jjmDg3HyeEip+YuoSElhfynnqbuyBHcJ08i8KGHLzp/Cq2hPB3SdxhhKj0RakuN5wL6wPC7oec0iJ4ILVyD8NNP4fXXjWBVWwthYXB2Sayf/rRFuxdCiE5BwpUQ7SS3Ope1yWtZf2o9ZfVlhLqHM9rrTg5/1Yt9Vc4Mi/Lh2bvimNEvGNMVllPQjY2UvvkmJf/3GiZ3d8L+8Hu8rr/+/CsJK3KMaujpO4xyCRXZxuOeocYVfbFTjYnoXi1bKqe+3iidMHMmODvDtm1GwLrnHrj1Vpg48UyhTyGE6CpkQrsQbchmt7EzdydrktawM3cnDnaYUzuQuKOu+BzLItUzhJM33ce9MwczOsavReUUGnNyyPnpUhqSkvC69lqCf/mksQSN1nA6EzJ2QeYuyNhpfA/g5m+MSMVMhujJENDrik/1feObQJWQAJs2QWUl/PvfcN11UFUFrq7gIH/WCSG6AJnQLkQ7Kq4tZv2p9aw9tZaCmgKuyvLkj19HEHQ8H+eGw1iViZLwOK7JOcx1Cc8TNuYPKHVl9Z8A7HV15Cz9Hyz5+US8+gqeg8Ih/QPY9oUxGf2bkSlXP+gxHsY+CNGTjKrorTh0lJJiFPasqgJfX7j5ZrjlFpg2zXje07PVDiWEEJ2WhCshWonWmn0F+1iTtIbPsz7Hqq2MCx3Hj/Rchr37KmUuNraHDcF14iRm3n0DgyKDqDt6lNz//TmZd95FwIMPEvDgEtRlDutoSz0FP19KQ9LXRN4Wi8eBH8OOMuNJj2CIGgfjHzJGqAL7tlqYqq6GDz+EdesgOtq4ui82Fn78Y+M04NVXw/fNmxdCiK5KwpUQLXS6/jQfpH7A2uS1ZFRm4O3szR397iDC4SrW7anH6Z0XqHR25/BvVnLntAH4uZ+pfuk6ZAgxG9ZT+NzzlLz6KjW7dxP2hz/gFBF+8QNWFULOfmPB4+x9lG/7ior9HgQMrMTDJw+iroUe44xQ5Rfb4tN837Vhg7FA8iefGKcAg4LggQeM50wmWL68VQ8nhBA/OBKuhLgCWmuOFh8lPimeTzI+odHeyLCgYdw/8AEaKgby1o4ckgoLmFP+FQNL0/F75hnG3jjsgvsye3gQ9tKLuE+aRMGzz5J5221Er3kfx7AwsDZAwTHIOQA5+4xQdTqr6YVO1Op+FB7yxH1EHwJeew28Qlr9veblwccfw733Gjntww/h4EFYtAjmzTMWSza3fI1oIYToMmRCuxCXobqxmi1pW4hPjie5PBl3R3euj72eG2Ju5lCKK2/sSCf3dB19gj1ZMi6cgU88gNnbm5h1a43FjL+P3U79gc/JXPIYjl5O9Jjnhvn0CbA1Gs97hUPESIgYDZGjsTqEk37L7ShnZ2LWJmD2vrKlcC7k669h40ZjceQ9e4zHjh6FwYON+VTu7nKVnxBCyIR2IVrgZOlJ4pPj2ZK2hTprHX39+vLMuGcYFzSdhP3F/GhVBuW1FkZH+/Hc3AFc1SeI0pUrKc7PJ+zFF88PVlobI1B5h5tuhyDvKC4NFUSMcSIr0Z+czRYiH34AU8wYCB8B3mdKlWurldx778NWUUH0mvdbHKzsduMUn5ubcbpv1izj8ZEjjcWSb7oJ+vUzHpNJ6UII8f0kXAlxEXXWOj7J+ISEpAS+LPkSZ7Mzs2Nms7D3QnzMsby5M4Nn/rmXOouN6f2CeXBqLCN6+AFgKSykZPXreM6YgfvoUVCWDvlHIP8o5DXd1zVNOjc5QshAGDQPwobhHjacsD2nyPvFk+RvsxJ27Y3nlGio2beP4uUvU3fkCKEv/g6Xvn2v6P3V1sLWrUa5hM2bjQKezzwDkybBK6/AjTdCZGQLP0QhhOiGJFwJ8R1pFWkkJJ1ZkibGO4Zlo5ZxQ9wN5JcrVn2WxqajiShgztBwlkyJpVdw03COtRFKkil+5jlobCAo9iS8GAUNlcbzJkdjUeN+10PoUAgbCsEDweHcNQO95w7EUlBE8YoVOIaGEvToI9QdO07xihXU7NqFQ3AwoS+8gM/cuZf9/rQ2Cnhu2mSMVnl5wezZMGaM8bybm1RKF0KIlpBwJQQXXpJmetR0FvZZyIigERzIPM0j/0rms6+LcHMy86Px0Tww3JOQ+lRI/TvsPg4Fx6H4a+qKoWJ7IP4D6nHy1NBrAYQMgtAhEDzgvCB1Mf6LF2HJy6N09WpqDxyg7tAhzD4+BD3+OL6334bJxeWS+7Db4fBhY2QqIwPeftuYlO7ra1zhd+ONMHkyODldak9CCCGaS8KV6NZyqnJYd2rdt0vShHuE8/Dwh5nbcy5+zv5sPVnI7eu2UZXzFSNc8lkfd5pBjjk4fn0SDhSe2ZFHCIQMRMdeReGrezH7VeH/9ifg6XXFbVNKEfLM01iLi6ndt4+ApUvx+9E9mD08LvnaL76AN94wruwrKDAC1fjx0NBgLEOzcuUVN0sIIcQltFm4UkpFAv8AQgA7sFpr/ee2Op4QzWW1W9mRs4P45Hh25e5CKcWUiCksjJvLeKcA7IVfk7zuJb7OOEqvxgzeMxVhctaggUIXCOwDPacb1c2D+0PwIPAIRNts5P3iCeqSswl98XeYWxCsvqEcHIh49RW0xYLJ+cIjXlpDUpIRpG67DUJDjSv71q0zinled51x2i8wsMXNEUII0QxtVopBKRUKhGqtDymlPIGDwFyt9VcXe42UYhBtqbCmkPUp61mXtJbCuiKCHD2Y5xLJzQ2akOJUdHk6StsBsGoTeeYwzCH9Cek5HHNIfwjsZxTlNJ//N8k3wapy82YCf/YzApYsbtP3Ul9vLID84YfGLSPDePz9943lZurqjPX7pEK6EEK0nXYvxaC1zgfym76uUkqdBMKBi4YrIVqN1lCZh734a/ZkfU584R62NRZiAybU1vFEVTVTarNwMCVj9YkhiSg+10M53hiGa3h/rr9qEpP7RTRrIWVts5H3RNsGK63h5EmwWGDIECgtheuvNyafT5sGjz9ujE5FRxvbu7q2ehOEEEI0U7vMuVJKRQPDgL0XeG4RsAggKiqqPZojupLaMihLg9KUc25l5elsdFEkeHqQ4+iIn83OPdqN+d79iIwZDIF9yHOIYuVxO2sOFtBoszOzfwhLpsYxNNKn2Yf/Nlht2kzgzx5u1WBVXm6MTn3yiXHLzoY5c4zinuHhsGOHUYeqGfPahRBCtKM2r9CulPIAEoEXtNbrv29bOS0ozqM11JUbAeqbW2kqlKUaX9eVn9lUmTjoH0W8lztbdQ0W7Iz07sXC3vOZ1nseTk1X6Z3Iq2BlYhpbvszDwWTi5uHhPDA5lrjAS08UP6dpFgv5Tz1FxQebCHz4IQIefLBFb9VqheRk6N/f+H70aNi/3yiVMG2aMTI1cybI3yBCCNE5dEiFdqWUI7AOePdSwUp0Y3YbVOYahTbLM6C86b4s3fi6vuKsjZVRqdwvFgbcBH5xVHiFsLk2i4TcbaRVpuPpCLfE3c6C3guI9YkFjLUAd6eU8FpiKjtOleDh7MADk2O5b0IMwV6XP/TTkJpK3uPLqD9xgoCH/ueKgpXWkJIC//2vcfvsM2hsNEasXFzgt781Tu+NGWPMnxJCCPHD0JZXCyrgTeCk1np5Wx1H/ABoDbWlcDoTyjPP3JdnGLeKHLBbzmxvcgCfKPCNgYhRRpDyizHufXqAowtaa46VHCM+KZ6Pj7xNg62BwQGDeW7Cc8yMnomrgzHpyGbX/OdEASsTUzmaU0GAhzOPz+rDHWN64O16+bO9td1O+TvvUPSn5ZhcXQlfsQKvWTOb/fqiImMkysUFVqyARx81Hu/Rw5iIPmOGUTYBYPr0y26eEEKITqAt/x6eANwFHFNKHWl67Emt9YdteEzREbSGmhKoyDLWyzudbdxXZDeFqSyw1Jz7Gjd/IyiFDYMBc42v/WKMQOUdAaYLL3JcY6lhS1I8CckJfF32NW4ObtwYdyMLei+gn3+/b7drsNpYfyiX1dvTSC+poYe/Gy/cNJB5wyNwcbzEAsoXYcnLI++JJ6nduxePqVMJfe43OFyivkF1tTE36tNPjaVmjh41Cnpef72xfp+TE1xzDfTseSZUCSGE+GFry6sFdwLy66IrsFmgMs8YYarIaQpR2UZ4Op1tPGatO/c1zt7G6JNfLMRdZXzt06PpPgpcLq8GVFJZEvFJ8fw77d/UWmvp49uHp8c+zXWx1+Hu6P7tdpX1Ft7bm8VbO9MpqmpgULg3r94+nFkDQzCbruyfo9aayk2bKHjuebDbCXnuN/jMn3/BKwkbGqCqCgICIDUV+vY15lI5OcGECfDCC2fmVPXrd2YxZCGEEF2HzOTo7rSGmmJjzlNFrhGUKnPOClK5UJWPUUHzLG4B4BNprJPXe6Yx2uQdCb49jHtXnxY37duFk5MT+LLYWDh5ZvRMbulzC4MCBp0Tbooq63lrVwbv7smkqsHKxJ4BLF84lAk9/ZtVTuFibKdPk//sr6n6+GNchw8n7KUXcTprNWOLxZh0/vnnxpyp3bvhrrtg9WqIiYFf/hImTjSqo7u5tejjEEII8QMh4aors9uNuU6VucbIU2Xuma8rco0QVZkHtsZzX+fgAl7h4B0OsVONEOUd0XSLMh53bLtCSmmn00hIvvDCyd7O3udsm15Sw+rtaaw7mIPVbmf2oFCWTI5jUIT3RfbefNW7dpH/xJNYy8sJfPRR/O+/D6vdTEqKcRoPYPhwOH7c+HrIEFiyxFivD8BkgmefbXEzhBBC/MBIuPqhslmhugAq843AVNV0X5l/JkhV5Z8fnEyO4BVqhKfwkdA/HLwijMDkFW4EKDf/dp8A1GhrZGvmVuKT4zlYeBAHkwMzomYYCycHjzhv9OnLnNOsTEzlo+MFOJpNzB8ZwaJJsUQHuF/kCM1nr6+n6E/LKf/nPyGmD/mL3mZjdgyJ18HOneDhAfn5xke0bJlxRd+UKcapQCGEEELCVWfUUGWEpKq879yfFaCqCznvVJ3ZuSk4RUDkmDMhyius6T4c3AONIZVOIqsyi7XJa9mYspHyhnIiPSN5ZMQjzImbg7+r/znbaq3ZmVLCysRUdqWU4uniwINT4vjRhGiCPFunkmZVUTX/vv0vDMj+F0F338VLhY+z4i7jx2TAALj3Xpg61RgUNJvhzjtb5bBCCCG6EAlX7clmNUJRVcEFgtNZAaqx+vzXungb4cgzFIIHgGfYueHJMwzc/H4Ql5xZ7BYSsxOJT4rni/wvMCszUyOnsrDPQsaGjsWkzg1/Vpudj44b5RRO5FUS5OnME7P7cvuYKDxdWrZ4XlUVbN/edPvcysEDLlj0k3z6p8kMfnQiPzoKk6+CSZNkZEoIIUTzSLhqDVobhS6rmk7JnROevhlxyoeaImhaGPhbJgcjMHmGGpPDe04zvvYKO+s+BJxafrqro+VX57P21Fo2nNpAcV0xIe4h/HToT7m5180EuQWdt329xUbCwRxe355GVlktsYHuvHjzIG4aHo6zw5WVU8jONk7tDR5sjETt32+URXB01Az0OMXd/nuY+cgIRi+aCBjzqIYMadHbFkII0c1IuLoUm6UpLH0TnM6+bxpxqioAS+35r3X1NUaUPEPOHW36Jkx5hRlX3XWi03StzWa3sTN3J/HJ8ezM3YnWmonhE/lVn18xMXwi5gvUs6qotfDO3kz+tiudkupGhkT68OS1/bimfzCmyyynUF8Pb74Ju3YZt6ws4/FnnoFf/xrGjoX/rCkmZPX9OJblEvnqK7iPG9wab10IIUQ31b3DVUP1WfOY8s7czp7bVFPM+XObnM6Eo9Ch0KcpQJ090uQZ2qZX1HV2RbVFrD+1nnWn1lFQU0CAawD3D7yf+b3nE+YRdsHXFFTU8+bONN7bm0VNo40pvQNZMiWOsbF+501o13Y71vx8GtIzsORk4zZiBPVBvdi71yiHEBAAS5eCoyM88QR4ehp1ph591DjFN7gpP5kL0oh+7cfYq6uJfOMN3IYPa+uPRgghRBfXvcLVf38FBcfOhKiGivO3+Wa0ySsUQoecNdp01v0PZG5Te7NrO3vy9hCfHM+27G3YtI2xoWN5fNTjTI2ciqPpwvOjUoqqWb09lQ2Hc7FruH5wKIsnx9E/7NxCo7aqKop+/3vqvjxGY2Ymur4egL+W/A+fVmtONWq0VphMMH++Ea7MZmP9vsDAc7tM22yU/u2fFK9YgcnNjR5/fxuXb6p7CiGEEC3QvcJVySmoKwf/OIiZdOYqurPnODlJpcfLVVpXysaUjaxNXktOdQ6+zr7c3f9u5veeT5RX1EVfdyirnJXbUvnPV4U4O5i4bXQUD0yKJdLv/D4ozaxgy51/YX9SCMddFlJoCWbXy5/jFBuD5c/9CN9bwMyKvzIiJI1pz8wifP5MvlkgIOg707kaUlPJf/KX1B09isdVVxHy7LM4Bp8/50sIIYS4Ekprfemt2snIkSP1gQMHOroZohm01uwv2E9CcgJbs7ZitVsZGTyShX0WMi1qGk5mp4u+bltSEa8lprEvvQxvV0fuGdeDe8ZH4+/hDBjLxRw/DgMHgoMD/PqJWp590QhcSmn691eMGwevvALOzmf2XXf0KAW//g31X32F27ix+N5yK2YfH8zeXpi9vDB5elL+/hpKXnkFk6srwU89hdf117WogrsQQojuSyl1UGs98rzHJVyJy1HRUMEHKR+QkJxARmUGXk5e3y6cHOsTe9HXWW12thzIoPR3v6Nn5jH+MPsR5l47mltHRVJf7cDWrbBvH+zdC4cOQV0dHD4MA0KLSLjxT+zLimTasqlMvncg3t9TfF3bbJSvWUPxij9jr6y84Dae11xDyDNP4yC1FYQQQrTAxcJV9zotKK6I1pojxUeIT4rnPxn/odHeyJDAIbww8QWu6XENLg4XL+BZ12hjzf4sPtiyl/u3vk7vsnq+tI4iZoNi8I0RuDs7sCsRbr3VGIUaPhwWL4YxYyDMMY/Mu+5lhKWEOR+8hvvogZdsqzKb8bv9drxvnIMlJxtbRSW2itPYKyuxVVTgFBeH59SprfjpCCGEEOeScCUuqrKxkn+n/puE5ARSTqfg7ujOTb1uYkHvBfTx6/O9ry2vaeTtXZn8c28GMce+ouKTQTxQ9y4FlhAATNgIf+ETRm2+lgkT4MAB4wo+x6Y57w1p6WT9+H7sVdVEvfUmrkOHXlbbzR7umPv2vZK3LYQQQrSIhKt2pLWmMT2dusNHqDtymLpjx3GKicZ34ULcxoxBdYJ6V1prjpccJyE5gY/SP6LeVk9///48O+5ZZsfMxs3xwhP+y8vh4EH4bKeFD7bWcuqEI66xZv4y4COGH/0P8/mQKdd6MXoyjBwJEdv+QsN7q6n8SOE1ezYjRpzZV+3Bg+T85KdgNhP19t9wHTCgnd69EEII0XISrtqBvbGR/KeeoiZxO7YKo/yDydsb1wH9qd39BVUffYxjZCQ+Cxbgc9NcHAIDv32tbmzEXluLcnLC5NZ2VzLWWGrYkraFtclrOVl2ElcHV66LvY4FfRYwwP9MuLEUFZH1ySEOJ3th8Y3gliUBmNzc6D/QTkGeCXDEw8POaJ+vuPH0Zkbv+wjfO27n5C9CMDmdmeSuxy0l86t95D/1NM59++IcEwNA5UcfkbfsFziGhRH5+mqcIiPb7D0LIYQQbUEmtLeDktWvU7x8Od5z5uA2ehSuQ4fiFBODMpmwNzRQ9Z//cjo+ntr9+8HBAcegIOw1Ndhra9EWy7f7MQcG4BQZhVNkJI49onAbPgL3sWNa1LaTpSdJSE5gS9oWaq219PHtw4LeC7gu9jo8nDywNzRQd/Agq5ZXsmWnPydKIym0Gqf2Ih2z+DhuFhVeAbxTfQP+6jSzLJ/jq8ox+/jgPnkS3tdfj8fkyRc8tiU/n/SbbsYhOJjoNe9T/u57FP3hD7iOGEHEK3/Fwde3Re9NCCGEaEtytWAHsRQWkTp7Nu7jxhH56ivfu21DejoV6zdgLS7G5OaGyd0Nk7s7Jjc37LV1NGZnYcnKpjErC2thIQB+99xD0P8+hnJs/gLGtZZaPs74mISkBI6XHsfF7ML0iNmMUHdQm9mbI0cUR45A8vF6/tvzGigr5rniX3HANomhfWsZPskTp6DT5Gck4pB7kl61RQy0lOLr7Y731Ml4TJmC6+DBKPOl1/+r3r6d7EWLcerRg8bMTDxnzyLsxRcxnV1jQQghhOiE5GpBwFZRgbZY0DY72G3GvbbjEBx8zimr1lS8/E9gsRC87PFLbuscE0PQY482a7/2ujqKlr9M2d//Tv2JE4S/vPyc04kXklyeTEJSAhu/3E5JWjgexdN5aPFN3DJ0Fi//zosFzxrbubho+gXkMb5xJ40+4cQ+/2v+OXo0NhdXNh3NY1ViEqeKqgmP7MUDt1/DwlGRuDld2T8lj8mT8V+8mNJVq/C7/z6CHnusU8w9E0IIIa5Utxq5Spszl4akpPMed+7Vi+i1Ca0+WlJ35AgZt96G/6JFBD36SKvu+xsVmzeT//QzmL28CF+x4py18axWqGmoJ7HgP7y2+QA7/zadhpzeWMpCvt0mMREmTzaKdn75JQzwzcD11UewpXyN7913EfTYY9Rh5l/7snhzZzr5FfX0DfFk8ZRYrh8chqO55UFIa40lOxunqItXcxdCCCE6Gxm5AgIWLzImlCsTmE0okxlrWSnFf1pO6apVBD70UKsdS9vtFDz/Ag5BQQQsXtRq+/0u7xtuwLl3b0795Oe8c9Mq8sctItVpGAcON5KcZCZ20Ys4j1qHj2U6vvUDGTfTg5HDjLIHQ4ZASFPO6t+7kbDD8RT94o/g7k7kqpU0jBjLisQM/v5FJhV1FkbH+PHbmwcxtXdgq1Y1V0pJsBJCCNFldKtw5XXttRd8vDElhZLX38Br9myce/VqlWNVbNhA/fHjhP3+JUzu7q2yTzBKHhw/DidOwLFjxqjTLbf0wfOv77J4gCdkgZ9rMbbYr/GfkcrYIT4sveZNRoWMQv38/EDUmJFBeXwCFRs2YCsvx33iRPSyp/n9V5XEv/QZDVY7M/oFs2RqHMOjZIK5EEIIcSndKlxdTNCyZVQnbif/mV/R4913Wjznx1ZVRdHyl3EdOhSvG264on1UVhoBSikYOxZsNoiLg8zMM9t4eRkjTxkVGSRUr2XQUxlMyznE/XvLafT3Iez55YQMG3fevu0NDVR/vo3T8Wuo2f0FmM14Xn01VTOu5081Afz7b8cxKbhpWDiLJsfRM8jjSj8KIYQQotuRcAU4+PkRtGwZ+U88wen4eHxvvbVF+yv5v9ewlZURvGrVJU+f1deDS9PqMc8/Dzt3GqEqJ8d4bNo02LoVzGZjiZiAABgwAPr0s5CkP2Vd8lpu2LgXB+XA9GlXMb/3H4nOdyL/0f/l9N2LcX7yCXxuvRWsVmr27KFyy4dUbd2Kvboah7BQAh56iLRRV/PisQoSdxfj7lTEfROiuX9iLCHeF1/WRgghhBAX1q0mtH8frTVZ995H/fHjxG7ZgmNwULNfa6+vp/7YMWoPHqL20EFqdu3G+6a5hD3//DnbHTwIe/bAyZNnbt7exj3AnDlGqOrf3whQ/fsbc6Oio8/sI7sym7Wn1rIxZSNl9WWEe4Qzr9c85vacS6DbmasFreXl5C1bRs32HbgOH05jWhq206cxeXriOWMGnrNns8srhpU7MziSfRp/dyfunRDNXWOj8XZrflkHIYQQoruSOlfN0JiZSdqNc/CYOpWIP6+45Pb1yckU/Po31H35JVgsWLQDRWETyA29irw+N5Kc4UpGBnz2mXF679574e23wdMT+vUzbkOGwCOXuJDQYrewLXsbCUkJfJH/BWZlZnLEZBb0XsD4sPGYTReuJ6XtdkrfeJPy9/+F2/AReF17LY5jx/LBiWJWbU8jrbiGKD83Hpgcy4IREbg4XroulRBCCCEMEq6aqWTVaopffpmI/3sVz6uvPu95raG0FE7sKmH3z98krTaCZYtLCZ4wiBc/Gsdv/3jmVFpoKPTtCxs3GvOjsrKM03thYUbYupTc6lzWJa9jQ8oGSupKCHYLZl7vedzc82aC3YMv631V1Vu+LadQWNlA/1AvHpwax+yBITi0QjkFIYQQoruRcNVM2mIh/eZ5VBXXUP/IK+Q49GPCBGPi+IYNcP/9xhV733B01OzfrxgyBA4fNq7g69PHCFXe3pd/fKvdSmJOIgnJCezO3Q3ApIhJLOi9gInhE3EwXd40ueKqBv62K51/7smkqt7K+Dh/lkyJY1KvgFYtpyCEEEJ0N1Ln6gIaGiA9HXx9ITjYmEj+0EOOJJ9cT06+AzTNa09I0Myfr4iJgQXzrAQd/BcRZV8w7o+L6T93CA5Nn+KwYcbtShTUFLDu1DrWn1pPUW0RQa5BLBq8iHm95hHqEXrZ+8soqWH1jjTWHszBYrMza0AIS6bEMSTS58oaKIQQQohm6VbhqqwMnnoKTp2ClBTjNJ3dDi+/DD/7Gbi6Qk0NTJ3mQFxUPYF73yE0ZQuDd/XDft3TDBnkxFPOP6O67jPCVy7Ha9aQFrXHZrexM3cnCckJ7Mjdgdaa8eHjeXLMk0yJmHLZo1QAx3IqWJmYykfH83EwmZg3IpwHJsUSGyjlFIQQQoj20K3ClYsLrFlj1IsaPx7uvht69TK+BoiNNa7ma9oabb+PkpWNlPz1FTLSvsK5T2+qt35K8C9/idesWVfcjoKaAjakbGD9qfUU1BQQ4BrA/QPvZ17veYR7hF/2/rTW7EopZWViKjtTSvB0dmDR5DjumxBNkJeUUxBCCCHaU7cKV25uxmT05lImE4E/+QmugwaR+78/pyEpCf8HfozfXXde9rFtdhu78naRkJzA9pzt2LWd8WHjWTZqGVMip+BouvzyBza75uPjBaxMTOVYbgWBns4sm9WXO8ZG4eUi5RSEEEKIjtCtwtWV8pg0iZh166g7eACvG2+8rNcW1Rax4ZQxSpVXk4efix/3DriXeb3nEekZeUXtqbfYWH8ol9XbU8korSUmwJ3f3TyIm4aFSzkFIYQQooNJuGomp4hwnCKad8rOru3szttNQlICiTmJ2LSNMaFjeHTko1wdeTWO5isbVaqst/DOnkze2plBSXUDQyK8ee2O4VwzIASzSa78E0IIIToDCVetqKSuhA2nNrDu1Dpyq3Pxdfbl7v53M6/3PHp49bji/RZW1vPWznTe3ZtFdYOVSb0CeHDKUMbF+Us5BSGEEKKTkXDVQnZtZ0/+HtYmr+XzrM+xaiujQkbx8PCHmRY1DSez0xXvO624mtXb01h/KBer3c61g0JZMiWOgeFXUEBLCCGEEO1CwtUVKqkr4YOUD1ibvJac6hx8nH24o98dzOs9jxjvmBbt+0j2aVZuS+WTrwpwMptYOCqCBybF0sPfvZVaL4QQQoi2IuHqMti1nX0F+0hISuCz7M+w2q2MCB7B0mFLmd5jOs5m5yvet9aa7adKWLktlS/SSvFyceCnU3vyownRBHhc+X6FEEII0b4kXDVDaV0pm1I3sTZ5LVlVWXg7e3Nb39uY33s+sd6xLdq31WZny7F8ViWm8VV+JSFeLvzy2n7cNiYKD2fpHiGEEOKHRn57X4TWmn0F+1ibvJatWVux2q0MDxrOg0MfZEaPGS0apQKoa7SRcDCb13ekkV1WR1ygO7+fP5i5Q8NxcpCFlIUQQogfKglX31FWX8amlE2sPbWWzMpMvJy8uLXPrSzovYBYn5aNUgGcrm3kn19k8rfdGZTVNDIsyoenr+vP9H7BmKScghBCCPGDJ+EKY5TqQOEBEpIS2Jq1FYvdwrCgYSwevJgZPWbg4tDyJWTyTtfx5s50/rUvi9pGG1f1CeTBqT0ZFe0r5RSEEEKILqRbh6vy+vJv51JlVGbg6eTJLX1uYV6vefT07dkqxzhVWMWq7WlsPJyLBm4cEsbiKbH0DfFqlf0LIYQQonPpduHq21Gq5AS2ZhqjVEMDh/LCxBe4psc1rTJKBXAws4zXtqWx9WQhLo4m7hzbg/snxhDp59Yq+xdCCCFE59RtwpXWmn989Y9zRqkW9lnIvF7z6OXbq9WO8XlSEa9tS2V/Rjk+bo48PK0X94yPxs/9youJCiGEEOKHo9uEK6UUiTmJ+Dj78MLEF5jRYwauDq6tsm+Lzc7mo3msSkwjqbCKcB9XfnVDf24ZFYmbU7f5iIUQQghBNwpXAK9Oe7XVAhVAbaOVNfuzeWNHOrmn6+gT7MnyhUO4YUgYjmYppyCEEEJ0R90qXLVWsCqraeTvuzP4xxcZlNdaGBXty3NzB3BVnyC58k8IIYTo5rpVuGqpnPJa3tiRzpr92dRZbEzvF8yDU2MZ0cOvo5smhBBCiE5CwlUznMyvZFViKpu/zEcBc4aGs2RKLL2CPTu6aUIIIYToZCRcXYTWmn3pZaxMTOXzpGLcnMzcMy6aH0+KIcyn9eZtCSGEEKJrkXD1HXa7ZuvJQlYmpnIo6zT+7k48NqM3d43rgY+blFMQQgghxPeTcNWk0Wpn45FcVm9PI6WomghfV34zZwALRkTi6mTu6OYJIYQQ4gei24er6gYr7+/L4o0d6RRU1tMv1Is/3zqU6waF4iDlFIQQQghxmbptuCqpbuDtXUY5hcp6K+Ni/Xlp/mAm9wqQcgpCCCGEuGLdLlxlldayekcqCQdyaLTZmdk/hCVT4xga6dPRTRNCCCFEF9BtwpXdrnks4SgfHMnFbFLcPCyCRVNiiQv06OimCSGEEKIL6TbhymRSuDqZeWBSLPdNjCHYy6WjmySEEEKILqjbhCuA3940qKObIIQQQoguTi6HE0IIIYRoRRKuhBBCCCFakYQrIYQQQohWJOFKCCGEEKIVtWm4UkrNUkolKaVSlFK/aMtjCSGEEEJ0Bm0WrpRSZuBVYDbQH7hNKdW/rY4nhBBCCNEZtOXI1WggRWudprVuBN4H5rTh8YQQQgghOlxbhqtwIPus73OaHjuHUmqRUuqAUupAcXFxGzZHCCGEEKLttWW4utDqx/q8B7RerbUeqbUeGRgY2IbNEUIIIYRoe20ZrnKAyLO+jwDy2vB4QgghhBAdri3D1X6gl1IqRinlBNwKbGrD4wkhhBBCdLg2W1tQa21VSi0FPgHMwFta6xNtdTwhhBBCiM6gTRdu1lp/CHzYlscQQgghhOhMlNbnzTHvMEqpYiCzjQ8TAJS08THE5ZN+6bykbzon6ZfOS/qmc2qLfumhtT7varxOFa7ag1LqgNZ6ZEe3Q5xL+qXzkr7pnKRfOi/pm86pPftF1hYUQgghhGhFEq6EEEIIIVpRdwxXqzu6AeKCpF86L+mbzkn6pfOSvumc2q1fut2cKyGEEEKIttQdR66EEEIIIdpMlw1XSqlZSqkkpVSKUuoXF3heKaX+0vT8l0qp4R3Rzu6mGf1yR1N/fKmU2q2UGtIR7eyOLtU3Z203SillU0rNb8/2dVfN6Rel1FSl1BGl1AmlVGJ7t7E7asb/Zd5Kqc1KqaNN/XJvR7Szu1FKvaWUKlJKHb/I8+3zu19r3eVuGBXhU4FYwAk4CvT/zjbXAh9hLDA9Ftjb0e3u6rdm9st4wLfp69nSL52nb87a7jOM4sDzO7rdXf3WzJ8ZH+ArIKrp+6CObndXvzWzX54EXmr6OhAoA5w6uu1d/QZMBoYDxy/yfLv87u+qI1ejgRStdZrWuhF4H5jznW3mAP/Qhj2Aj1IqtL0b2s1csl+01ru11uVN3+7BWPBbtL3m/MwA/A+wDihqz8Z1Y83pl9uB9VrrLACttfRN22tOv2jAUymlAA+McGVt32Z2P1rr7Rif9cW0y+/+rhquwoHss77PaXrscrcRretyP/P7Mf7CEG3vkn2jlAoHbgJWtmO7urvm/Mz0BnyVUtuUUgeVUne3W+u6r+b0yytAPyAPOAY8rLW2t0/zxPdol9/9bbq2YAdSF3jsu5dFNmcb0bqa/Zkrpa7CCFcT27RF4hvN6ZsVwDKttc34Y1y0g+b0iwMwApgGuAJfKKX2aK2T27px3Vhz+mUmcAS4GogD/quU2qG1rmzjtonv1y6/+7tquMoBIs/6PgLjr4fL3Ua0rmZ95kqpwcAbwGytdWk7ta27a07fjATebwpWAcC1Simr1npju7Swe2ru/2UlWusaoEYptR0YAki4ajvN6Zd7gRe1MdEnRSmVDvQF9rVPE8VFtMvv/q56WnA/0EspFaOUcgJuBTZ9Z5tNwN1NVw6MBSq01vnt3dBu5pL9opSKAtYDd8lf3u3qkn2jtY7RWkdrraOBtcBPJFi1ueb8X/YBMEkp5aCUcgPGACfbuZ3dTXP6JQtjNBGlVDDQB0hr11aKC2mX3/1dcuRKa21VSi0FPsG4quMtrfUJpdSSpudXYlztdC2QAtRi/JUh2lAz++UZwB/4v6YREquWBVDbXDP7RrSz5vSL1vqkUupj4EvADryhtb7gZeiidTTz5+U54G2l1DGMU1HLtNYlHdbobkIp9S9gKhCglMoBfgU4Qvv+7pcK7UIIIYQQrairnhYUQgghhOgQEq6EEEIIIVqRhCshhBBCiFYk4UoIIYQQohVJuBJCCCGEaEUSroQQQgghWpGEKyGEEEKIViThSgjRJSmlRimlvlRKuSil3JVSJ5RSAzu6XUKIrk+KiAohuiyl1POAC8aCxjla6991cJOEEN2AhCshRJfVtO7bfqAeGK+1tnVwk4QQ3YCcFhRCdGV+gAfgiTGCJYQQbU5GroQQXZZSahPwPhADhGqtl3Zwk4QQ3YBDRzdACCHaglLqbsCqtX5PKWUGdiulrtZaf9bRbRNCdG0yciWEEEII0YpkzpUQQgghRCuScCWEEEII0YokXAkhhBBCtCIJV0IIIYQQrUjClRBCCCFEK5JwJYQQQgjRiiRcCSGEEEK0IglXQgghhBCt6P8ByenWIr/rb64AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(X_test, te_pred, label='DML default')\n",
    "plt.plot(X_test, te_pred1, label='DML polynomial degree=3')\n",
    "plt.plot(X_test, te_pred2, label='DML polynomial degree=10 with Lasso')\n",
    "plt.plot(X_test, te_pred3, label='ForestDML')\n",
    "expected_te = np.array([exp_te(x_i) for x_i in X_test])\n",
    "plt.plot(X_test, expected_te, 'b--', label='True effect')\n",
    "plt.ylabel('Treatment Effect')\n",
    "plt.xlabel('x')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Model selection\n",
    "\n",
    "For the three different models above, we can use score function to estimate the final model performance. The score is the MSE of the final stage Y residual, which can be seen as a proxy of the MSE of treatment effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DML default': 1.815769478666336,\n",
       " 'DML polynomial degree=3': 1.6913528143752934,\n",
       " 'DML polynomial degree=10 with Lasso': 2.222578476206349,\n",
       " 'ForestDML': 1.9002757666765648}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score={}\n",
    "score[\"DML default\"] = est.score(Y_val, T_val, X_val, W_val)\n",
    "score[\"DML polynomial degree=3\"] = est1.score(Y_val, T_val, X_val, W_val)\n",
    "score[\"DML polynomial degree=10 with Lasso\"] = est2.score(Y_val, T_val, X_val, W_val)\n",
    "score[\"ForestDML\"] = est3.score(Y_val, T_val, X_val, W_val)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model selected by score:  DML polynomial degree=3\n"
     ]
    }
   ],
   "source": [
    "print(\"best model selected by score: \",min(score,key=lambda x: score.get(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DML default': 0.3565984526892961,\n",
       " 'DML polynomial degree=3': 0.257796113358552,\n",
       " 'DML polynomial degree=10 with Lasso': 0.26248739267870214,\n",
       " 'ForestDML': 0.42857317657348887}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_te={}\n",
    "mse_te[\"DML default\"] = ((expected_te - te_pred)**2).mean()\n",
    "mse_te[\"DML polynomial degree=3\"] = ((expected_te - te_pred1)**2).mean()\n",
    "mse_te[\"DML polynomial degree=10 with Lasso\"] = ((expected_te - te_pred2)**2).mean()\n",
    "mse_te[\"ForestDML\"] = ((expected_te - te_pred3)**2).mean()\n",
    "mse_te"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model selected by MSE of TE:  DML polynomial degree=3\n"
     ]
    }
   ],
   "source": [
    "print(\"best model selected by MSE of TE: \", min(mse_te, key=lambda x: mse_te.get(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Changing only Final Stage Model Specification and Refitting\n",
    "\n",
    "It is also feasible to change the parameters of the estimator and only fit the final stage model using the existing first stage residual calculation. To enable this feature, the `fit` method should be called with the flag `cache_values=True`, so that the original fit data and their corresponding first stage estimates be stored in the model. This can be done for any estimator in the dml module. We portray an example below, using the `DML` estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Final model doesn't have a `coef_stderr_` and `intercept_stderr_` attributes, only point estimates will be available.\n",
      "Final model doesn't have a `coef_stderr_` and `intercept_stderr_` attributes, only point estimates will be available.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Coefficient Results</caption>\n",
       "<tr>\n",
       "   <td></td>  <th>point_estimate</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X0</th>      <td>5.953</td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>CATE Intercept Results</caption>\n",
       "<tr>\n",
       "         <td></td>        <th>point_estimate</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cate_intercept</th>       <td>0.5</td>     \n",
       "</tr>\n",
       "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"
      ],
      "text/plain": [
       "<class 'econml.utilities.Summary'>\n",
       "\"\"\"\n",
       "Coefficient Results\n",
       "=================\n",
       "   point_estimate\n",
       "-----------------\n",
       "X0          5.953\n",
       "    CATE Intercept Results   \n",
       "=============================\n",
       "               point_estimate\n",
       "-----------------------------\n",
       "cate_intercept            0.5\n",
       "-----------------------------\n",
       "\n",
       "<sub>A linear parametric conditional average treatment effect (CATE) model was fitted:\n",
       "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n",
       "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n",
       "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
       "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = DML(model_y=RandomForestRegressor(),\n",
    "          model_t=RandomForestRegressor(),\n",
    "          model_final=Lasso(alpha=0.1, fit_intercept=False),\n",
    "          featurizer=PolynomialFeatures(degree=1, include_bias=False),\n",
    "          random_state=123)\n",
    "est.fit(Y_train, T_train, X=X_train, W=W_train, cache_values=True)\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Coefficient Results</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>point_estimate</th> <th>stderr</th> <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X0</th>       <td>-0.054</td>      <td>0.685</td> <td>-0.079</td>  <td>0.937</td>   <td>-1.18</td>    <td>1.072</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X0^2</th>      <td>7.253</td>      <td>0.695</td> <td>10.437</td>   <td>0.0</td>    <td>6.11</td>     <td>8.396</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>CATE Intercept Results</caption>\n",
       "<tr>\n",
       "         <td></td>        <th>point_estimate</th> <th>stderr</th> <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cate_intercept</th>      <td>1.289</td>      <td>0.154</td> <td>8.374</td>   <td>0.0</td>    <td>1.036</td>    <td>1.542</td> \n",
       "</tr>\n",
       "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"
      ],
      "text/plain": [
       "<class 'econml.utilities.Summary'>\n",
       "\"\"\"\n",
       "                   Coefficient Results                    \n",
       "==========================================================\n",
       "     point_estimate stderr zstat  pvalue ci_lower ci_upper\n",
       "----------------------------------------------------------\n",
       "X0           -0.054  0.685 -0.079  0.937    -1.18    1.072\n",
       "X0^2          7.253  0.695 10.437    0.0     6.11    8.396\n",
       "                       CATE Intercept Results                      \n",
       "===================================================================\n",
       "               point_estimate stderr zstat pvalue ci_lower ci_upper\n",
       "-------------------------------------------------------------------\n",
       "cate_intercept          1.289  0.154 8.374    0.0    1.036    1.542\n",
       "-------------------------------------------------------------------\n",
       "\n",
       "<sub>A linear parametric conditional average treatment effect (CATE) model was fitted:\n",
       "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n",
       "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n",
       "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
       "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from econml.sklearn_extensions.linear_model import DebiasedLasso\n",
    "est.featurizer = PolynomialFeatures(degree=2, include_bias=False)\n",
    "est.model_final = DebiasedLasso(fit_intercept=False)\n",
    "est.refit_final()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Coefficient Results</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>point_estimate</th> <th>stderr</th>  <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X0</th>       <td>-0.068</td>      <td>0.759</td> <td>-0.089</td>  <td>0.929</td>  <td>-1.316</td>    <td>1.18</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X0^2</th>      <td>7.26</td>       <td>0.744</td>  <td>9.761</td>   <td>0.0</td>    <td>6.037</td>    <td>8.484</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>CATE Intercept Results</caption>\n",
       "<tr>\n",
       "         <td></td>        <th>point_estimate</th> <th>stderr</th> <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cate_intercept</th>      <td>1.29</td>       <td>0.159</td> <td>8.116</td>   <td>0.0</td>    <td>1.029</td>    <td>1.552</td> \n",
       "</tr>\n",
       "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"
      ],
      "text/plain": [
       "<class 'econml.utilities.Summary'>\n",
       "\"\"\"\n",
       "                   Coefficient Results                    \n",
       "==========================================================\n",
       "     point_estimate stderr zstat  pvalue ci_lower ci_upper\n",
       "----------------------------------------------------------\n",
       "X0           -0.068  0.759 -0.089  0.929   -1.316     1.18\n",
       "X0^2           7.26  0.744  9.761    0.0    6.037    8.484\n",
       "                       CATE Intercept Results                      \n",
       "===================================================================\n",
       "               point_estimate stderr zstat pvalue ci_lower ci_upper\n",
       "-------------------------------------------------------------------\n",
       "cate_intercept           1.29  0.159 8.116    0.0    1.029    1.552\n",
       "-------------------------------------------------------------------\n",
       "\n",
       "<sub>A linear parametric conditional average treatment effect (CATE) model was fitted:\n",
       "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n",
       "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n",
       "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
       "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from econml.sklearn_extensions.linear_model import StatsModelsLinearRegression\n",
    "est.model_final = StatsModelsLinearRegression(fit_intercept=False)\n",
    "est.refit_final()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CATE Intercept Results:  No intercept was fitted!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Coefficient Results</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>point_estimate</th> <th>stderr</th>  <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X0</th>        <td>5.566</td>      <td>0.347</td> <td>16.029</td>   <td>0.0</td>    <td>4.995</td>    <td>6.137</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X0^2</th>      <td>2.243</td>      <td>0.451</td>  <td>4.975</td>   <td>0.0</td>    <td>1.501</td>    <td>2.984</td> \n",
       "</tr>\n",
       "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"
      ],
      "text/plain": [
       "<class 'econml.utilities.Summary'>\n",
       "\"\"\"\n",
       "                   Coefficient Results                    \n",
       "==========================================================\n",
       "     point_estimate stderr zstat  pvalue ci_lower ci_upper\n",
       "----------------------------------------------------------\n",
       "X0            5.566  0.347 16.029    0.0    4.995    6.137\n",
       "X0^2          2.243  0.451  4.975    0.0    1.501    2.984\n",
       "----------------------------------------------------------\n",
       "\n",
       "<sub>A linear parametric conditional average treatment effect (CATE) model was fitted:\n",
       "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n",
       "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n",
       "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
       "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from econml.sklearn_extensions.linear_model import StatsModelsRLM\n",
    "est.model_final = StatsModelsRLM(fit_intercept=False)\n",
    "est.fit_cate_intercept = False\n",
    "est.refit_final()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Final model doesn't have a `prediction_stderr` method, only point estimates will be returned.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Uncertainty of Mean Point Estimate</caption>\n",
       "<tr>\n",
       "  <th>mean_point</th>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>0.697</td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Distribution of Point Estimate</caption>\n",
       "<tr>\n",
       "  <th>std_point</th> <th>pct_point_lower</th> <th>pct_point_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0.398</td>        <td>0.075</td>           <td>1.319</td>     \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<econml.inference._inference.PopulationSummaryResults at 0x137774de2e8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.model_final = Lasso(fit_intercept=False)\n",
    "est.refit_final()\n",
    "est.effect_inference(X).population_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Example Usage with Single Binary Treatment Synthetic Data and Confidence Intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. DGP \n",
    "We use the following DGP:\n",
    "\n",
    "\\begin{align}\n",
    "T \\sim & \\text{Bernoulli}\\left(f(W)\\right), &\\; f(W)=\\sigma(\\langle W, \\beta\\rangle + \\eta), \\;\\eta \\sim \\text{Uniform}(-1, 1)\\\\\n",
    "Y = & T\\cdot \\theta(X) + \\langle W, \\gamma\\rangle + \\epsilon, & \\; \\epsilon \\sim \\text{Uniform}(-1, 1)\\\\\n",
    "W \\sim & \\text{Normal}(0,\\, I_{n_w}) & \\\\\n",
    "X \\sim & \\text{Uniform}(0,\\, 1)^{n_x}\n",
    "\\end{align}\n",
    "\n",
    "where $W$ is a matrix of high-dimensional confounders, $\\beta, \\gamma$ have high sparsity and $\\sigma$ is the sigmoid function.\n",
    "\n",
    "For this DGP, \n",
    "\\begin{align}\n",
    "\\theta(x) = \\exp( 2\\cdot x_1 ).\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Treatment effect function\n",
    "def exp_te(x):\n",
    "    return np.exp(2 * x[0])# DGP constants\n",
    "\n",
    "np.random.seed(123)\n",
    "n = 1000\n",
    "n_w = 30\n",
    "support_size = 5\n",
    "n_x = 4\n",
    "# Outcome support\n",
    "support_Y = np.random.choice(range(n_w), size=support_size, replace=False)\n",
    "coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
    "def epsilon_sample(n):\n",
    "    return np.random.uniform(-1, 1, size=n)\n",
    "# Treatment support\n",
    "support_T = support_Y\n",
    "coefs_T = np.random.uniform(0, 1, size=support_size)\n",
    "def eta_sample(n):\n",
    "    return np.random.uniform(-1, 1, size=n)\n",
    "\n",
    "# Generate controls, covariates, treatments and outcomes\n",
    "W = np.random.normal(0, 1, size=(n, n_w))\n",
    "X = np.random.uniform(0, 1, size=(n, n_x))\n",
    "# Heterogeneous treatment effects\n",
    "TE = np.array([exp_te(x_i) for x_i in X])\n",
    "# Define treatment\n",
    "log_odds = np.dot(W[:, support_T], coefs_T) + eta_sample(n)\n",
    "T_sigmoid = 1/(1 + np.exp(-log_odds))\n",
    "T = np.array([np.random.binomial(1, p) for p in T_sigmoid])\n",
    "# Define the outcome\n",
    "Y = TE * T + np.dot(W[:, support_Y], coefs_Y) + epsilon_sample(n)\n",
    "\n",
    "# get testing data\n",
    "X_test = np.random.uniform(0, 1, size=(n, n_x))\n",
    "X_test[:, 0] = np.linspace(0, 1, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Train Estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "est = LinearDML(model_y=RandomForestRegressor(),\n",
    "                model_t=RandomForestClassifier(min_samples_leaf=10),\n",
    "                discrete_treatment=True,\n",
    "                cv=6)\n",
    "est.fit(Y, T, X=X, W=W)\n",
    "te_pred = est.effect(X_test)\n",
    "lb, ub = est.effect_interval(X_test, alpha=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "est2 = SparseLinearDML(model_y=RandomForestRegressor(),\n",
    "                       model_t=RandomForestClassifier(min_samples_leaf=10),\n",
    "                       discrete_treatment=True,\n",
    "                       featurizer=PolynomialFeatures(degree=2),\n",
    "                       cv=6)\n",
    "est2.fit(Y, T, X=X, W=W)\n",
    "te_pred2 = est2.effect(X_test)\n",
    "lb2, ub2 = est2.effect_interval(X_test, alpha=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "est3 = CausalForestDML(model_y=RandomForestRegressor(),\n",
    "                       model_t=RandomForestClassifier(min_samples_leaf=10),\n",
    "                       discrete_treatment=True,\n",
    "                       n_estimators=1000,\n",
    "                       min_impurity_decrease=0.001,\n",
    "                       verbose=0,\n",
    "                       cv=6)\n",
    "est3.tune(Y, T, X=X, W=W)\n",
    "est3.fit(Y, T, X=X, W=W)\n",
    "te_pred3 = est3.effect(X_test)\n",
    "lb3, ub3 = est3.effect_interval(X_test, alpha=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99117512, 0.00264324, 0.00244716, 0.00373448])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est3.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Performance Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mzZoxR2be8pa3UCwWqVarvOhFL8pECvMsW7aMD37wg8O+/5vf+Aa35N7z29/+tm4dIQiCMBLf+MY3eO9738vAwACrV6/ma1/72pDXFAoFbrzxRj7wgQ/Q3d1NFEV86EMf4pRTTuGtb30r3d3dGGP48Ic/THt7O694xSt47Wtfyw9+8AO++MUvAvD5z3+eb33rW9mc/f/8z/8wf/78uusopbK51JQ0L33iiSfq4trnP//5urbjo0FNZtZ7pKxfv96k6rIpjz/++JD2GWFikX9jYbLRGk4+Gc44A2655cjeq5TaYIxZP/YrZw/DxTpBmMmxeOXKldx3333Mmzdvum9lVlCNYlxH4TnDN6YN999aYp0gTC0zOebOVMpBhO86eK5DFGs8d/Tm2yONddLKKwjCpPPjH8OWLfCWt0z3nQiCIEw+xgAzZ99fEARhQtCmVjHVk1DclFZeQRAmnf/7f2HxYnjVq6b7TgRBOBpStXlBEATh+EUbQ2wMnjFE2uBPsMibVEwFQZhUnnoKfvQjeM97oFCY7rsRBEGYGqRgKgjCsUikbVIaxfb7RCKJqSAIk8qNN4LnwTXXTPedCIIgTA0zSL5DEARhQtHaUA1jtDEEkZ5QlV5JTAVBmFQ+9jHYuNG28gqCIBwfGCmZCoJwzGGw7byRtpZYsZ5YayxJTAVBmDSMAaVARO8EQTieEO0jQRCOSUzNp9kYg8FMqAiSJKajcOjQIdatW8e6detYtGgRS5cuzR4HQTCp1960aRPr1q3jnHPO4emnn+YLX/gCp512Gm85ClnTa6+9loGBgUm4S0EYGWPgec+Df/zH2rHeSjh9NyQIwqjI37yJRlJTQRBGZrbGXJMkpenP/zyBMVdUeUdh7ty5PPjgg4A1EW9ubq4zmY2iCM+bnH/C73//+1x99dV8+tOfBuBLX/oSP/rRj1i1atURn+vaa6/lrW99K42NjRN9m4IwIr/6Fdx1F7zjHbVjfdWIlpI/bfckCMLIyN+8Z49JFCplxlQQhLE4FmKuNvDFL36Bd7z9bRMScyctMVVKrQFuyB1aDXzSGHPtZF1zKnjHO97BnDlzeOCBBzj33HNpaWmp+x/pzDPP5NZbb2XlypV861vf4gtf+AJBEHDhhRfypS99Cdd16863YcMGPvKRj9DX18e8efP4+te/zgMPPMC1116L67r88pe/ZM2aNWzZsoWrrrqKd77znVxzzTW8//3v5+GHHyaKIj71qU9x9dVXE8cxH/vYx/jJT36CUop3v/vdGGPYvXs3L3jBC5g3bx533HHHdPyzCcch114Lc+bAm99cOxZPsHqbIAiTi/zNO3ok2gmCcKTM9Jj7Fx//GD//6e0opfj9d7wTYwx7JjDmTlpiaox5AlgHoJRygV3Azc/2vJddNvTY618P73sfDAzAy1429Pl3vMN+HTwIr31t/XN33nnk9/Dkk0/ys5/9DNd1+dSnPjXsax5//HFuuOEG7rrrLnzf533vex/f/va3edvb3pa9JgxD3v/+9/ODH/yA+fPnc8MNN/AXf/EXXH/99bz3ve+t+x/xxz/+MXfccQfz5s3jz//8z3nhC1/I9ddfT1dXFxdccAEvetGL+OY3v8nWrVt54IEH8DyPzs5O5syZwz/90z9l7xWEqWDLFvj+9+HjH4f8BprW03ZLgjDrmAl/70D+5h0JBlBIUioIsxGJuWPH3Ge2beWOu+7B8zwOd3bSMWcOX/mXL0xYzJ2qVt7LgaeNMc9M0fUmlde97nVDdiQG8/Of/5wNGzZw/vnnA1Aul1mwYEHda5544gkeeeQRXvziFwMQxzGLxyFdevvtt3PLLbfwD//wDwBUKhW2b9/Oz372M9773vdmZf85c+Yc8e8mCBPB//2/4Lo2mEOtvS2W/jZBmHXI37yjQEKdIAhHyUyOue/8w3dnMbdjEmLuVCWmbwS+MxEnGm33obFx9OfnzTv63Ys8TU1N2c+e56FzZaBKpQLYhfjb3/52/vZv/3bE8xhjOOOMM7j77ruP6PrGGP7rv/6LNWvWDDmulDqicwnCZPAHf2CVeJcutY/D2FDwFLHW8v+pIIyTmfD3DuRv3hGRlkxrPwiCMEuQmFt730gxl0mOuZOuyquUKgBXAd8b4flrlFL3KaXuO3DgwGTfzoSzcuVK7r//fgDuv/9+tm7dCsDll1/OjTfeyP79+wHo7OzkmWfqC8Zr1qzhwIED2f8wYRjy6KOPjnnNK6+8ki9+8YuZItYDDzwAwBVXXMFXvvIVoijKrgnQ0tJCb2/vs/1VBWHcnHUWvPvdtcfpbGmsIZI5U0GYtcjfvNExg74LgiA8G2ZazL3+3/5fFnMPpzG3eeJi7lTYxbwUuN8Ys2+4J40x1xlj1htj1s+fP38Kbmdiec1rXkNnZyfr1q3jy1/+MqeccgoAp59+Op/5zGe44oorWLt2LS9+8YvZs2dP3XsLhQI33ngjH/vYxzj77LNZt24dv/nNb8a85ic+8QnCMGTt2rWceeaZfOITnwDgXe96F8uXL2ft2rWcffbZ/Md//AcA11xzDS996Ut5wQteMMG/vSDUE8fwgQ/Aww/XH4+S3b5YGxFAEoRZjPzNG0rNNsF6+gmCIEwUMy3mLlt2As+/aD2XXnw+//U9q3H7zne9a8JirjKTPPOllPpP4CfGmK+N9dr169eb++67r+7Y448/zmmnnTZZtycg/8bCxHHzzfDqV8ONN8JrXlM73jUQ0N5Y4OGd3aye30RTcfxTBEqpDcaY9ZNwu9PGcLFOECQWz06iWOO5DtrYjTffdegphxR9h6I3/JzYcP+tJdYJwtQiMffIMMbQORAMaQlpLHo0+BMT6ya1YqqUagReDNw0mdcRBGFmcO21sGIFXH11/fG0fTc2Rlp5BUE4pqjb35fwJgjCMYjWhjA2kx7jJlX8yBgzAMydzGsIgjAzuP9++OUv4R//ETyvVkUA28JrkmqClsRUEIRjiKx919R+lignCMKxhDaGKJ58z7+pmDEVBOE44NprobkZ/vAP7eN8ZTSINNVI2xlTsYwRBOEYoq5gaoY7KgiCMLsxMCXrt1mRmE72HOzxjPzbChPF8uXw4Q9DW5t9nBc5qoQxlTBOKqfTdIOCMAuQmDwLqRVMa6q8o/xnlP/GgjBzkM/j+Ei73o70PUfKVPmYHjWlUolDhw4xd+7cmedXNssxxnDo0CFKpdJ034pwDPCZz9Q/zldMy2FMbyUSxUpBGAX5ezc7Gb5iOsJr5e+uIMwYJOaOH21sO+94OdpYN+MT02XLlrFz505mo8fpbKBUKrFs2bLpvg1hFtPXB7/8peGlL1V1vsvpzlp/NaJrIORgX5WCO7xqmyAI8vdupjOSt3wQaQqek8xgGQqew0AQUXCdbM4+j/zdFYSZgcTc8RPGmiAafsa04Dn4ExTrZnxi6vs+q1atmu7bEARhBP7t3+BDH1Lcdx+cd17teOpdGsaaMNZE2qCY/MF5QZityN+7mU2agA7mge2HOfWEdnqrEU/v72PNsnZ+8NAuzls2h+VzG6fhTgVBGA8Sc8fP3U8fYuvB/mGfO3dRO6cuap2Q68yKGVNBEGYmYQj/9E9wyXNNXVIKkOSlBMkumxGrGEEQZjFpF8hgZfFI29kro2uvmQLxSkEQhEkniq1w5UhJ6UQz4yumgiDMXL73Pdi+Hf7x8xqtHZSycwiRtlVSgCg2BLHGdx2MrNYEQZilpIqU2hgcaj29UWzQiVVMGNtJerHFEgThWKCvGg3bpjtZSGIqCMJRYQx87nNw2mmGK15qk0+A7nJIJYyJtKEcxDYxjTSuo5C1miAIs5WsGmpMtngyxqCzL/ua9LEgCMJsp68a0ViYunRRWnkFQTgqdu2CQ4fggx82GAw9lZBqqNnXU6G/GlMOYg72VQmSNpBqGHPftk6qUTzdty4IgnDEpIlpPudME1FIDOiTbhHZhBME4VigHMTs7ipP2fWkYioIwlGxbBk8/TT0VGLCWFs7mKKdt4p0TF/Ftn8YbCVhy8F+ntjbyy+ePMBbLlwx3bcvCIJwRGQzprnM1Foo2GTVJBXTMJKsVBCE2Y8xhnIYc6C3OmXXlMRUEIQj5uBBaG2FQgF0NaYaWIGjA0GVw/0BDQWXchhTDmvVUd9VGAOd/cE03rkgCMLRoY1JWnfrj6Vf6YxpNZauEEEQZj9hbKhGekqFK6WVVxCEcRHnAtMHPgDr1lnl3WqoKYe1qmlPJcxmSweqEV0DgVVzMwqD9TUVBEGYbUQ6N0eaq54aU2vxjbVNTgVBEGY75cCOZcWSmAqCMNOoJNXPbdvgu9+Fl73Mqu+Ww5hKGBNEmkoYUw40+3qqRFrTH8T87PF9PLiji+5yCMC+nqlrCREEQZgodGoLk1ZJs8e1ymmkNeEIJvSCIAizid5qSDWquSxMBZKYCoIwJsYk7Ryx5vOfB6XgQx+yam3VUFONbCtvvnU31oaBIOJw0robi0qlIAizmFToyBgbz9IKqrWKIaumBmKLJQjCMUAljKlGMdVQElNBEGYAJlcVCGPNjt0x/+//Gd70ZsOixZr+akQligkjm5zmFXdTu5iqVA8EQZjFpG27sTE1OxhNXfXUGPs6bZjS6oIgCMJkUQk1A0EsM6aCIEwf+UVVGBt6qxGxsV6k//EfikoFPvQncTIUb3fSQm2V2/JqlLE2WSUB6i0WBEEQZgs6myE1Njk1hlBrIq0TRV4b6NKW3qmcxxIEQZgsqpHVC5lKJDEVBKGOfBCKtK2Kag1BrPmD94T89Bchq0601dFKaL+HUf2u2qG+Kjfdv4vO/iBLSDft7Z2OX0cQBOFZkeaZsYY4tolnEOla9dTYBVwqhDSV1QVBEITJwPrPT333h9jFCIJQRxBrGnAB244bRJrYGCqBpug5nHJGRDVyiE1SMY00ivpK676eCgAHeivT8SsIgiBMGGnFVKcVU20wRoPnZKJH5TDOktSprjAIgiBMJJUwRimmZRRLKqaCcJyTzkml5BNMo21g6uk1XPmcZr73HTcbhh/IhI9iwkSZsoay74e6cx+rKKU+rJR6VCn1iFLqO0qp0nTfkyAIz468JUz63VrFkFVMTdLC21+Nsw6RvmrIzQ/s4keP7Kmbuz8WkFgnCMc+PZXQ2gFOQ/ySxFQQjnMGz0Tld/t1UhX98lc0u7Y7rFhlRY66yyH9ibBREA21R1Aq+cFAX/XYWpgNRim1FPgAsN4YcybgAm+c3rsSBOFoSTfToiwxTY/bY5HWBIlvc9rOu6+3Qk/FWmId6rNK5I/s6jmm5k0l1gnC8UEYp3P0Ux+/JDEVhOMcY2oLL6ivmGpj6OvXXPcvPudcGLBuvU1GD/UFtmIaxezrrnLfM53Ze6JY05lYxGw92D9lv8c04wENSikPaAR2T/P9CIJwlKTxMLW/SpNLY+yMqU7E4CphzMG+aqbIGyTib062MweNhWNuYkpinSAc48SxIUy6QqaaYy5iCoJwZOhBbbz5QKQNfP97BfbvdfiTz/SjjU8QaXoqIUXPIYgNv3rqIABrFrZy28N76s5dGWY+4ZXrlk7SbzI9GGN2KaX+AdgOlIHbjTG3T/NtCYJwFKQV0DCsmcqnFVSDrSLEyYiDMYaDfdWs6yR9vT5Gxxck1gnC8UHNp1kqpoIgTDGpxQHYual8IIpjw7eva+CMsyNOW1/GGGseX400PeWIWNcSzy0H+8Z1vSXtx9ZIklKqA7gaWAUsAZqUUm8d5nXXKKXuU0rdd+DAgam+TUEQxkGamFZT1d2cj7M29vlI66Riar/SGNo1EHDLQ7v4wYPHZhFRYp0gHNtkns2J8KWeBktmSUwF4TjHYDIvvsGJKQr+8vNdfPiT/dlcVTWM0drupoW5edTH94zPDibf5naM8CJgqzHmgDEmBG4CnjP4RcaY64wx640x6+fPnz/lNykIwthYyxcgmye1X1klNdaZfVYljKnk1Hgf29NDrGsV1pedtWh6f5mJR2KdIMxC+qvRqM+nCWltrt7GulgqpoIgTDVpFWAgiBMrBDsnahIbhKUnVTnxjCpxokRZjXQ2g3U0tgjHXl7KduAipVSjUkoBlwOPT/M9CYJwFKT2LwZT51kaJQq8sbaWMWGkibRONvVI2t5q5wAouMfcEktinSDMQvpGSEzThDRMSqNxrmIaJR0jU80xFzUFQTgyUlP4/mqUta0Fsea7N2r++BqfQ4eSZFTXBD9SUr/SI0EdY5mpMeYe4EbgfuBhbFy9blpvShCEo0Ibq0hpjF2shdrOmqabdWn1NDaGIK5VF/K2WOlaTmKdIAgzgcGzomm8So9XAp10wtUS1CBXhJhKRPxIEI5ToljjuQ7VUOM6ikP9AR2NBWsWX9V86q88glDx6pJNTGNjqES6rn23pxwe8XWdY2utBoAx5q+Av5ru+xAE4dkRa0NfNaLBd20CGtfaeQ21ikK6UWeM4Yl9faxZ1EJfxVYlTJaYTtMvMYlIrBOE2cfgjtxYGxxlN9G0Nhzqr+I4tgsuzhUopgNJTAXhOCWMDZ4LlTCm5Lsc7g9oa/CJtOGmm2DT4w6f+ec+HBeqYYwx0J1LRCthzDOdA0d8XcUxuFoTBOGYwBjDQJKYhrFt4Q1yQkja2IQzNoYottZZj+zuobcasr+3as+RnEsinSAIM4HBFdM4VzGNtLXG0hr6gwjHUUS6Zvs31UgrryAcp6S7YZUwJsgrTsaGf/g7j5UnRlxwufUhTaukaUUA4N6tnfRWRh+oH45jsYogCMLsRydVgjDxKk0Fj9KKqU7Ud3USJ9MkFaC/Gg8537HWyisIwuxkcMVUa5ucWrVxq0JuMFRCzUAQEcWGgWDk9V0Qae54Yj+9lZD7tnWy4yiKFCMhiakgHGekXnvVKCaMrd1BqsZbCWNuv9XnyU0Ob3pPH+XIqk4+sP0wt23cjTGGwwMB92w9xKGj2E1z1DGpyisIwizEDFqtRdpQDXU2Z5UKHoWxzoSQ0kTVJHVRN5lNGK7tTUKdIAgzgSGJqamJuWlTmyethDH91TgrUozE3p4KXQMhj+3pYcfhMjfdv+uoNEeGY1ITU6VUu1LqRqXUJqXU40qpiyfzeoIgDE8Q1RZNA8nOfk85ohzGVKOYcmAtD8phzNrzAt71oQHWXdrHzx/fx48e2cu2QwMEseHXTx3kzicOsLtr5AB0yUlzaW/064698NQFAHiOksWaIAgzgkpYi4s6WaBVwrhOgddgK6T7eytEuuZZOnihN9wiTkKdIAgzgXwrr07E2/KdINVkXr6cWGCFSTwczDOH+ikHte6Q/FpwotZ2k10x/Wfgx8aYU4GzEVlxQZgW8lLh/Ul7Rnc5pLcSEWnDvp4KxtiFWnNHxBuv6SUyekgCerBv7CrpvOYiL1izIEtGAToafTxH4TrqmBQ/EgRh9lHOKYwbkoppMk9qqLXrGqjNmSYtvumaLV26RcNUTKU7RBCEmUA+xUzHEcIkpsXabsgZYCCIKYdx8lz9OYJIc//2Ln7z9MFhfVGLnjsh9zpp4kdKqVbg+cA7AIwxATA9k7SCcJzTWwlpb/Az02SwyWpfxVrEVKMYB4c/+19FGtbu4coXFJjXXDzi61x6yvxsMdbW4PO8k+fRNRDiuw6Xn7aA7rL9WRAEYbqpRrXENNZW9KgSxrb6mbTwGpNax5jMfD7WprbQS34Ih2t7k7xUEIQZQF3FNO/HHGuKnpP40xvKQYyrFAf6qiOeq6cS0bO3d8jxojcxa7vJVOVdDRwAvqaUOhvYAHzQGNM/idcUBGEYKqH14zOJR18ljIm1pq8aEsTWDua3tzdyxw+beM5ijx2dA0ecmK6Y28icpkLdsXnNReY1F/E9hxVzm2jwXTxHElNBEKYHY0wmSpQq7TqOItaG/iDO2ntTa5i0dTetLqTnSOdT02R1Guz+BEEQxkW+K1ebpJU3Zw2TOi+UwwjXUUNGFdL3jcZEJaaTuUL0gHOBLxtjzgH6gT8b/CKl1DVKqfuUUvcdOHBgEm9HEI4/0naLShJ0wtiaxR/qD4i1bdvoHgg53Bfyra80sWhlhSVnd+IdRVWzsTByG0fBVZR8x7bySl4qCMI0kSaXYJUpdZJkxsbQX40SixibnAax3cwLIk3nQGBnsmJT1+L266cOTvWvIAiCcETkhd6MsV9RbBIFcjJbrCDSdSMOeSojHE+ZKBXyyVwi7gR2GmPuSR7fiE1U6zDGXGeMWW+MWT9//vxJvB1BOH5Ig1DqO1qNdFYZDWNNfzXKlNi+u2EHX74+ZOdWj+e9YT84VqToSBn8DkeRiSAVPVsptTOm0t8mCML0oAdVDnTSRRLHJouR9rhNQg0mU6iMtU1aY2OkQioIwqxhcNyLE2usdG7eGOiphMQaKsHwCegvN49cPHzuyfMm7F4nLTE1xuwFdiil1iSHLgcem6zrCYJQQxsyrz27qNKEkV10RdpWBowxdPYHhCE8dtsyVpwccOL6bqBmgXBk1N6jFHiuk1VRm4oevmvFj47u3IIgCM+e+lmr1P4lSTh14uunk4qC1lk1AWyFIYpt+69kpoIgzGS0ro91KfGguKcHFTIiPXxwG0bfLWOi2nhhcmdMAd4PfFspVQC2AH8wydcTBAEbhBQQJkEn1LZloxpaf6owsibyYWyII1h1yX6WnRjw1ME+wKaYgz3+xkIpKHgOQaTxXQffVRQ8+72l5FGNNApRqhQEYfoYnJimc/dBrJPWNisGklVMTc02YSCICCOD5yoaRhldEARBmG60MTgousthNktqEkXetJU37RoB6K+O3qo71rUmiklNTI0xDwLrJ/MagiDUqEYxRc9NElNFFFv7gyiZLQ2S7+UwztR5Hd+w5ordQ841wqbZqDQXXXqNoeA5uEpR9BxKvsucpgIHeq3KmxRMBUGYLupb2mz1IJ2tsvOliUVMUjFNbWQAbt24F7Ax7FXnLB37YlJVFQRhGtC6lnD2VmqJaTnn0xwka0CddYSMUhIdgwnMSyfdx1QQhCmkHMSZvYHB7v73VsKsIrCzs5wFpzDW/OLWRrbfO29IUAljPapceJ7FbSXAVlkbCx6NBY+C6+C5Ct+1c6Ulz7XJqrTyCoIwjeQ7QdL5qjCuJaZ2kZb6lSZm9FoPFQ8Z4zq9e0vc9xt/cn4JQRCEUTDUqphRbJINN2sHo3M+zWkMhJFbeMd7vYlCElNBOEaIkkH2aqQz1bVYG3rKEZG2i679vVWqoWYgiOntVXzzn9t55rfzGdxd+8S+Pu5++tC4rttU9FAKfM/Bdx0aCg5F31ZKPcfBcxRF36GYtPVOlHKbIAjCkaCzaqhdRmljMJqsmySI7Exp+gVkSrx5n1ID3HT/rlHHHVoWVTjx1KEm9IIgCJONSQSOtE6tYez6sBLGxMYwEMTZsVSpPB4lMR1rtGvWtPIKgjB1pC1oaXBRWHlvG4BsIAJsK0dsuPHrTVR6PS65evuzum6D7+IomNNUoOQ7KOVhDEnFVFHyXVs19d3hTegFQRCmgHTxFMTajjwkdjH7eqr4rqq1tuXmSvNV1cGMtJDr2tFI27IBOuZIvBMEYepJRdvipIMuTiyxgljjuYq+asRAENNQcOs24Qaz8/AAv9t2mNMXt4x6PWnlFYTjmDg3DxBEtRazKBlsT5V3tYG9PRUqyUxBOUlMD/RW6epU/Py77Sw95xAdK/qf1f2snt/Ec0+ax9nL2mhr8GkuejQUXJuwOoqmooeTzJtKF68gCNOFwe78B1EtAY2NoRrFVhwuO16rEISxti29w6y87t/eNeRY57Ymfva/z2brXQsm7fcQBEEYjdTmqiZ4ZDtGqqFVHQ+SdWIY61zL79DNt6f2W0HMx/b0jnitOJjYVFISU0GYJeQXSmAT0a5ykFUho9i2oVWjmGoUZ/MEtkJqZ02DSPOjR/Zy/b+UiAKHM16xY8zrjtR525SoUjpKsXJeE20NBVobfAqeQ0vJJqeeo2guejjKmi/7roQcQRAmDz1CFTOtfEaJQnkQ6Zxtgn0cJbHU5JQqd3YOcPMDuzIrhTy7usp1j42BB7+3imJrwAnrD4r2kSAI00Iq3hYnVdM4jXNxTKTt6ELW7puNNgw9z3DLv7OWtnHV2UtYNa8RgO0/Wc1n372UcGiIPCqklVcQZgmxtjYFWcU0UVarRDGuo5LWDbu4ch3FQDXOElVt4PBAmL3XX9LJaS+t0Lq4PNolmdPoE2lDT2XorNTlpy3EGIPrWAGklpJHU8GjpxxS9G3FtBLae1PKih45UjIVBGESSS0SBhPnLBLAUHVrQnFxbDAmrjtHuljbnFQM9veMLQa343fz6NzSwvrffwq/pDMfZ0EQhKkmipOkNJk11doQRLYzJIhqiuPRCCNWxhg6B4Zmm46yXvdrl7WzrNTOD/9nLi95RYQ/QVpvkpgKwgwnijWe6xAbgwdZEIkTD6r9PRXaGwvJaw2VKMZ3HboGAgAqoa2wGlNrR1t2bifLzu0c89qtDT6Hk/OkXLR6Dg3J3KijFK7jUHBdWhv8RODIoeQ5WaU07YBzlcKTxFQQhElkJP2OdFMu7TiphCpr0Y20wcnbyGj7Zd9nv5sx6p9R1eHhm5fTsbyPFRcdAGBec/HofxFBEISjJPVhjmKTzdKnSWrXQFizCzQMOz8PcN8zh4c9PqfJrjcdpbjz+21UKw7v+qMAKEzIvUtiKggznEgbPDc3W5qsmMKkTaMcaFpKtZa0dIYgbT3r7Au475lOFraWKPW38/iPlnLyC/fgFcf2rHIdRXtjge5yxHnL2znQV2VxWwnPcYi0rcz6rsJzFY0FO1PaWvLxXEXBc9DGzYJemsgKgiBMFiOpQ6ZxM0rmrpTKtbppUzfYlK+YViNbSR1L3KN3XwNGK85+3TaUTCwIgjCNpK28YayzhDTWhlBrtFEEkS14aMOwopRhrNl5eGhH3boT2rNCCEClX3Hh5f2ctOboPVAHI4mpIMxwqpGm5Lt1LbxQa02rRHGSkNrZgWpkh9nTxPTOJ+3ufd+Bfp781mqe2FBi1SX7xpWY+q7DxSd2sGreAB2NRZbPbaKQ2L5E1Zh5LUUqQYzn1OZH2xr9bIi+5Nda2TzxMBUEYZLJJ6Zxsnlm29hIvPus6Iej7IIsXcCpXDZpDDxzsJ9DfVUqSWK6Y5hFWp6O5f289DP34/oyWSoIwvSgtcFxFAYb/1JxI50IIIWRBtchjHVtBGxQxdQYw60b9ww592WnzKejqb4q+tYPdSWbdu0T9jvIvp4gzHBSNd1UXS3KJajaJL5UsaES2vnSahRTCTXloD7YHNjcwsa7mllzxW5KrePz12tt8JjbVOTE+c1ZG67vWoVdsFYxRd/BGyRqlH+cJqOOJKaCIEwy+VbezPKlTvzDVklTpfIweZxPaLUxfOd3O/jl5oOZgu9o7Lx/DjpWQ5JSaRARBGEqSSNQula0rbympkGi0+pp2hlCZpOVMtI4hO/V1nVhAFset0nqRMc5SUwFYYbTH9gkMk4WVVHuK9bWnzSIYypRTH81ohzEdA0EBJHOqqbGwMabVtDQXuXky4fuhA3m5AXNnLu8nTWLWmgpeXZutOCyqK2I7zoUPZei51Dw7HzpaLOj+eckMRUEYTKxgkb1hvE2EU0S0qRKmra29VfjOpN5oM4Peix2b+zgt/9vDc/cPZ8XnioWMYIgTB95hd0oNlkiqpN23rS1F+wcfWoxmMeMMLfg59Zvv/rvJj7xB4t4+rGJmSvNI628gjDDGagmFdNkmD2ItLWASVp5y0HM0wf6rSJuJaQSxgSR5tHd3Ww7NADAzg1zObzNqkV6hdErAAo4c2kbAAXXoS0RQFrQUszaQYqew5ymAgXXIXStYfOI58ttp4n4kSAIk4nBLsrcnPCazs1YRVrjOQ6xtjG1txqiE3XePHGW3I58rThwePC7K2ldPMCKiw/gqPl1z4sKuSAIU0kWx4zVISkZJ/FlhnJo/ZoLbpq82srp/p4qUdLae98zhzltceuw5y4mo1lxBLd+q5XVp1VZfVow7GufDZKYCsIMpxzGaF3b7eqpRHQ0aXorEU0Fa8kSxppKGNNfrVkeHOyr2Rs0L6iw6pJ9mVpknotWzyGMDZv29tBfjWnIWRy4jkNHU4Gdh8ssbW9gX0+F2DcUfXvccxRKeeMWNRLxI0EQJpODvVUa57iAypLLMDJZtTSIDG4hXZTppGJaP5tqconq9s6BEa+16fYlDBwq8fwPP4rjDq0ySF4qCMJUksYxQ1oprYm59VUiu5ZMX2PsJt4tD+0G4PyVHezprgzbLbKorZT9fO8djezb6fOhvz0wKeMKkpgKwgynEsaZqloYm6wq2lMOKXp2N0zHBmNqW/s95ZBKrj2jY3k/5711y7Dn72gsUPJdTuho4Ml9vaxfOYfexLfUdaCl6OE6isVtJTr7A2JtaGsoAIaC69JQcMedcErFVBCEySSIdTZndbg/oMF3qcZxNlcaxpqicQBrFzOQG5UAG29/9MjeMa/Td6DIEz9ZysoLD7H6rH7SfUBFbc5LNuIEQZhqTDI7GsW1ThFj4FB/kLX2QqLaGw5NQkcbYtAabvlGK0tWhJx36eiCcEeLzJgKwgwiinVmr6Jz81FxoioZaZ3NkUZa1xkj3/TALu7ZegiAn2/ab6sD/S4P3LCScvdQ5+MFLUXOW9GRVUgdR7FmUSstpdp+les4OJklTDJT6jm0N/q4joPvqcyvdDxIa5sgCJOFzs1TgVU0D2NrJp+28YaxzmZMde61JvHzG09SChBVXTpW9HHlH+zNqqtKKZuZkj6e0F9PEARhVNKxhfx3bQwGkxUc0pi34ZnD3JZT301tY4abMT0rGe/at9Pj4F6Pq97egzNJGaQkpoIwhehhWiTy9AdxlmyGie9eGmAirbOWtL5qlFVPU2Jt2N1VqTvfph8v4+lfLKLaOzQxPXd5B8vnNOI5CkdZhV3PUXiOg6OgueRlFc5UZddP/ElLyWsLroMraruCIEwzUWqLoGtzVmmVNIptt4n9rpOKQmo4b1+bGtKPl/ZlA7zgo49yydrmugpDPhJKxVQQhMkiv55Mf7b2VzXHhjQx3XZwgL6qTUyroaYaxuzuqtTFrgd3dGXnGExz0RYsFi+PuPbmXTzniv5J+Z1AElNBmFLi4T7xOfqrUc0IPq6pq6VD61aV19A1EBLGmt1dQ1sp7n/msD3XoSJP3bmIFRcdoH3Z0DkpPxEsKni2Ktra4NHa4OM4trLZ6NfUdmvfHYqeYxNY1yaknuPUCRwJgiBMNVEyOxUn86Gpankc1yqlUTIOoY1dyBlTr2I5HuLA4ZFbTiAYcLn4xLm4jqIpWbS5SmVV0kHFU0EQhAklPxefWr5kFdIkQe2rRBzsrfLbLYe4+2nbUffTx/bx34/szawHB9NVrhc0aky66roPOWgNTS0GdxIHQSUxFYQpJPUiHYn+apT5ToW5QLPrcJn+IKIaxQwEEf1BRH81Yn9vlcP99UHkmUSs45HvL0cpwxmv2DHstdIqZ1PBw1WKku9S8h08x1ZEHQdKiQqbn1RMXUdRcF2Usoq9StnkVAqmgiBMJ6lKeaw1BsPBvsBWQbUmTCqlsbZV1XrLhFo87q+O7e/82H8vZdOPltG1s4lFrVYQ5IKVc7hw1RwaCi4r5zYBNimViqkgCJNFqj0CtdEEA7k5Uttdd7DPrhHTtWfa0jvieQcpkTf4LsbAP3x0Pp//2LyJ/jWGIImpIEwhaWvZSFTCQTv7yWKruxxSCWIe3tnNDx/aw67DZfb1VLnziQPc+eRQpd2DTzez4755nPyiPTR2DC/nnVY5W0pW3KjBdyn5Lq4DTQUXpRRzmqxHVVpd9ZJWXlfZ70rZaqq08gqCMBWMNA4RxzXj+HReNLOIiU0WU9PndNbCa79Xo5hfPXVw1Gt372rkyZ8uYcXF+1lwSk92vOA5LGlvAOC5J9uFm6MkMRUEYfLQuibaliakRttYqBMBpHIYZ3YxqcbH+AcWajx0d4ktjxc597mTI3iURxJTQZhC8jtcw3F4IKCaE+oIknmogSCiHMZsPdiPATr7A361ubaI2jHI0qBpbsCJl+7l1Ct3ZUPrzUWXF566gJMXNHPu8nbAJqUtJR/HUTQU0sTUoaloLWDmNReBWsXUc6z4kaMUvmsTVMdRsgATBGFK0CN0nIRJNTQV/EhbeyNtCJNW3jCuf832zgGqobXY+u+HRxc9Mho2/Mdq/MaYta9+pu65/L5cGisHCyEJgiBMJLHJi7cl8/PYuKcTC6xyYOPb0SSjKdrATf/WxtxFEc972eTNlqaIXYwgTCFaGyI1NESkSpEDQUQ10lQjK4Jkf7aqko1Fl3IYD3NWuC+ZK01paA84541bAThxfhPGGFbOa8J3HVacPI+ugZCC59Bc8mgpefhJxRTsbGtz0aPBN5lir+fWZk39ZCbVM7VVlySmgiBMBZE2eO7Q46lnX5y0s8XabuqlFVOtIYg02lj1yb5KwIZnDrOwtcgJHY1jXnfLrxfSuaWF89+xmWJzfSuc7zpUE3uutLtEKfExFQRh8oi1wVW1KmhaJU033lKf0tHGx8ZD39NzePrRIu/8WCfeUB3NCUcSU0GYQiJtUFi1tHR+E6ytQRTXktBqZKulQaT53oYdBJFhQUuRkj96k0NYcdjw7ydy+st30rrYtlwopTh5YUv2moaCS9dASGPBxXccir5DY9Gj4DnJnIKmoeDWbfZ7iS6456o6pV6deKfKAkwQhKlgpDVWlMxYZeJHSVuvTUy17UCJahXTnmTO6nB/wL6e6pjXXXhaF6e+dCfLLxja7uu5imqUbNy5DqvnN7GsvSQbdoIgTDhaGxxH2Xjn1AfEfLfIs0tHa2z5zQLmLIh4/u/1TdAZR0cSU0GYQrQ2RGhIXF7S5DROlCKDpFoaJAnqQBhlC7H9vVUWt1mxjSf29g57/k0/WsbO++dxyot3D3nOdxVhbGhMrtnW4OM6VvSotWRbdwueQ5hUTCu56mw2Y+o4dfOkaZIqCzBBEKaCkZTNo1gTe04yl28yc/lIGypRTKzJRiMibWMrQDAOixhjoHl+lTOvGl5IzrbvxjiJhda6E9qZ11yUuCgIwoQTaUPBUcTG4OZ8mFH2u06FkNLnnuX13vepQ+zf6eEXnuWJxonMmArCFBKbevPj/T3WdzTUdq40jK0Jcqq++6OH99bNpHYN2Iy2Pxja0tu3v8Tm/1nMiov3M2dl/RyA69TacX3PfuxbSh5Fz9q/zG0qZkq7nqtsNdWthQcvp8rr5hZb6TC9IyVTQRCmgJFm9COdN5VPVXoN1SjmYF9AbyWkmrTyRnFt9mosdj/czl1fOpVq38j7+H4SN13HxldF0l3iSlwUBOHIGU2LJLUUjBOxIyDLPg21GftsHv9ZZKZxqPA8WLJybMXyiUISU0GYAEYLInlSdcjUa2pn4kOaqkcCHOqvUgk13eWQp/f31fnrDTdjun5FBwAbb1qB4xrOvHr7kNekgkUAvuPgKGgseDQUXIqeS1uDj6MUnuskz6thK6O+qyQJFQRh2tB6eAG5VMHctvIaYm3VzauRRmtDJdTs7a6w/dBA5ms6FlHF4cH/XM3AoSJ+aeREtqlgPZ8dpSgmG38lr35zTxAEYbz0jWJdlTo7xEnLLtiE1CTquybZfEvzUp3LTCsj6JQMx4EnW/nRJ85l25NTMFiaQ6KmIEwA6Q7WYAZbG6Q7+gY7V9o9EHKwr0oQ1RZK5cDOmPZWohHFjvJ4rmLfplZ2PzSHU1+6k4a2MHsuXSS1NfiZPYznKpZ2NNBc9CiklVBXZcloyXdwnFr7LpC9TklrmiAI08hIyubp8Vgb9vdWCZNW3mqos829X20+yH3PHM4sZcbi0VtPYKCzyLlveRrHG/kNzSUvETtSib+z7UgRBEE4GkZLTOM4512a+jGnSWgyWxpqncW4fLz8+eP7xnV9Y+DRH54AwJLlU1ctBUlMBWFCiEaYU4oTq5fssTaZZUHXQEgYa3rKNjnNB49qFNNfjUf07MvjOQ5zV/Vx1que4eQX7smOX3n6Qv7wuSsBaG8sZC24vqtoLHi0N/rZjr6b7PQXXIf2xgL+oFlSqZIKgjCdpPEx7TYZ7vl046+/GhFrYwXkRqiMjpWYHtrSzOb/Wczq5+1l3olDRT8WtBSznx1V29jzXQeFkmqpIAhHTfdAOOL6L0wKIVZ1txYXMbVj+aT1t1s6AeitROOaqQfY93gbB59q5czf20WhVHvPVIQ12dIThAkgGiGARLHh8EBIY8GzFjDJjn4h0vRWwmzxVA5iWkqaShjzP5v2c/GJc9jXUx3XaEDBdfCKmjVX7M4dUzQWPVbNa2ZvT5Wmgpcp56btZiXfpZio/BYTC5gw1niOQilZWAmCMHOoRjGNBQ+tyeKUl4tRaYVUG9u+G8baWm4xtOukGsZjjl88cstyGjsCzhrkWXrR6jnE2rC0vSE7lq+UpjFVwqcgCEdLNYrpCyJaS0PbaPObdFm7rjbg2HHSKDaZBeHRYAw8estyGudUOPl5B4BF2XOe4xCP0CE4UUhiKggTwEiLHOtHaj/EXQOh9dMzhu5ySDmwiWoQacphzI7OMj96xJq83/mEtSRYllv8DEe52+fv37uM1VcPMHd1H20NPt3lkEtOmgdAR2MB33Wy2dCORh/PcSgmbWlF1yr0phXRNCkF6iqmgiAI00nabRJpjdYulUjTnMv+BqoRzUUvm+OvhHHSnTI0jv13EmdH4+J3P8FAZxG/pDl/ZQe/22a9ohe31cdk31W0lGp2W36SoIoiryAIR4NJukLCaPgE0M6PmvrE1CTCR2mcjA1Haxhz4MlWDj/TzPrff4rmxvodNt+reTZPFpKYCsIEEI3QLhbEOlHaDW1iqjWOUnQPhJSTXftqkpj+6OE9Q97fVBzGST7HwzetYPfWAm84tYkTVpZ4YHsXYGdBPUeBssJHvmsFjRa0FfE9BdjzFrz6oJOfIZWKqSAIMwWDtdNKBT/CUNNctEuYroGAvmrE3OS5WGsqoR4xMR2Ngc4CpbaQQlNMoWkAGD3JLPkunptabWkKnkPBc2RjTxCEo8K248JITR1xIuaWJqMmN96Qdo08m4rp/FN6+JPP76F1dciyOXPrnvOnIK5N6spTKbVNKfWwUupBpdR9k3ktQZhORmrlrYZ2h7+7HNJXDTNF3t5qRDXxLK2GtpV3uF2ocJR2s/1PtrL93vm8/K09nH2Gx+K2hiyQOcqKHDkKWpPdfEfBvOZiZm0AQxNTQRCEmYgx0JNYvsQ5nz5jDAd6q9liTOt6D9O0m2Xrwf4xrgA6Utz1pVP5zVfW1B0frfjpOXa2tOA6eI6d0y/5rlRMBUE4arSuTzbz9AfWUjD1gUljIpDFvM37+/jN04eO+LpG23h3xvoKJy1souTXF0emomAxFRXTFxhjDk7BdQRh2hiplTeINWFkqLqannJEc8nDVSppN9PEGgaCiIEgHnZ3LB5hUF3Higf/cxXzF0dc9fYewCaia5e1sXFnF40FL6uSnji/Gd91WNRWYnFbiYEwznbSZFdfEIRZgYH+akQY20WYUnZB5ihFfxI/U59SbUxiG2MT2t5KyIM7usa8RPl3p9C9q4kzBlluKaU4f2UHXeUAsBt/jlJE2mQWW01FD4hwHTvDL7FVEISjwUAWx8C6PrhOLUEMsjhn41s11FmsSyumv9p8kCDSPLG3d9zX1TH8z9+fxcqLD8CZw7/Gn4JihpRLBOFZYhIz4+EIIk2oNdXQChs9srObvmpEpDVBUiHtq0bc+cR+eitDJbmf6RwY9rzb751Hz55G3vaRw5Qa7LV912Fha4krz1iE76ps1mlZRwO+q1jW0Yjn2h19X4zfBUGY4eQ3/Ax27CH1Kk1FjspBzEAQJQs0K3QUJdVU+7OmZ5jYOpiunY385NsdrHluJ0vO6qp7TgHLOho5f+WcLCltbfDxXDsy4bqKuc0Fmop241FaeQVBOFrSTpA0/A0ufOhcAmqtB+NMrTwteKTx77E9PeO+7vZ759O1o5mGjuqITgzDrR0nujlksiumBrhdKWWAfzXGXDfJ1xOEKSe/szWYINKEkUYBA2HEzzft5zdbDnHZKfOzec5KqOmrjt/0GOCP3u2x8ax9vOAKTWOhxJ7uCp6jspbdxoJr/UhVTWE3bckoJiIdgiAIM5kg0jQUbNwySUXUc1SiQKmymdNKGGMwme+zXbTZczxzaID7k9n7kdCx4r5vnkhza8zzfn8nXYOmKtL2tYLrEsUGz7VCcpUwpujZiqmrFE1FD8dRuIl9jCAIwtGgDZldzODCR5a4JnEqSmfrc17OI61JR7xepHjstmXMX9XPO95UyGKeUvXWWoVcK6/rQKwnvr13siumlxhjzgVeCvyRUur5g1+glLpGKXWfUuq+AwcOTPLtCMLEE2md7U7lMcnMUxBr9vVUbJKaiCT95mnb3b63p0I5OLKkNCq7FHyH9c+r0lh0OXVxC40Fl4LnsLS9AUcp2hsLNPhutpOVDyZKqWzGVBAEYaaQLsTSOJn3IE3Fj7SpVUSD2HaipKMQldC+PooNfZWI7rIVnRuLaq+Pjh3e9OGDnLaqNOT5/Ey+5yoafI/2xgJKQWPRw3Ws6nlbg5+9XiqmgiAcCek60rby5mZMB410GWzXSCp8FOtaoppVUo+w9rD1rgUMHCrxgrfsp7lUq1m6gzbYXEfVWQ+CjYuKiYt3k5qYGmN2J9/3AzcDFwzzmuuMMeuNMevnz58/mbcjCJNCGNsgoLWhHMTZ4ioV30gXR9Yqxh7f3xvwu62d3P30Ie57pnPc19r7WBu3/eW5bHm8ANiEc3FbA20NPkXPZWGrFTdqKXk0Fb0sWAxuy/Ad6eIXBGFmkSaiqRBcKmYEtY2+1Dw+ig1xbKuklVAniWpSMTWG2x/bx/9s2j8uw4SG9oAX/flGzrt0gEWtJVbMaax7vuTl/J6VoqnoJuJyivYGH8+xP6eJqaPUkAWdIAjCaOQLowbqBI2y48Ykwkhkc6V2ttTGvbzg27ivG8Omnyxl7ok9vPxlg50aIL989BwnW086yiapBXdiix2TtjpVSjUppVrSn4ErgEcm63qCMJVEca36GUY6CwiHBwKqkSaKk5392FANaz8D9CXzTju7ygDjrpjGgcODN6yisSVi5ckBjqq1UDQVPYq+Q2uDT8l3aC35tDX4jLRpP9L8gHB0KKXalVI3KqU2KaUeV0pdPN33JAizjTQhTWdFg0hnyaqBxALBZDZbkbYV09TwPY2x412YxYHDIz84gbDs4rgm2/Mf3DpX8Bw8x45EeI5Dg+9m7brzWopZ+25aJbUV1GfzLyEIwvFGWiE1Jq2Y2uP5eJb+mFftTaurcWySeHhkDqaOC8/9o8c5541bh3R6uE79WILj1Cql6eacn2iXTBSTOWO6ELg5maPzgP8wxvx4Eq8nCFNGGKeCQxBqnansVsKYahSjlIsx9rmsCpBbYOXpHyMx9RyFNoZHfrSUvv0NfPa6Q7Q02VmndBC9sWDbduc1Fyl6Ls3JrJPMOU0Z/wz82BjzWqVUAWgc6w2CINQzEES0ljwquYpppA3VyCqJB1Gt66QaxbSU/MzPTxtDfzXi0d09LO1oyJ115CXaw99fzlN3LGbBqd0sWFMTCTljSSuNBZeTFzRzuBzSUHDRGgqeTT4918la2lIv1XwiOngxJwiCMBb5udB0jnTY49Qnrmliqo31N912qH+0sFeH0TZ2tS0tJ0c66p53HVWXGHuOQ5qDeo5D6Jg6DZOJYNISU2PMFuDsyTq/IEwnabLZgEsQ2cVTmLSVDQQxvutQCWN+8shelrY3MKepMKL1y1h4ruLg9hJP3L6EFRfu53mXKQYCl6rSWcU0HxSKvoPr2h18WRtNPkqpVuD5wDsAjDEBEEznPQnCbKSc2r7kKqZhpAGHSOustTdOxiYAK3xkQGu444kDDAQx2w7V1MyHUzsH2LeplafuWMxJl+3JktJUkK6p6HHJSfMI4phi4knquHYh5qdKvLmZUqi33nIdmTEVBGH8VHI2fgY7L6qzxzXyVdRa4mrjn12HarYd7B93xXTjTSuo9vi84aN7WLO4JTvuOdYOSyUq42EyJuHmKqa+q6iEDImFzxZpNhGEIyDdxbIzTjY57a/aVrIo1lRCTddASGwMOw4PsO1QP7/cfJB7tnayr6dyVNc8bVErex+ei98Ys/a1zzCvuUjJdym4NmCArZgWk59LXq3NTElmOhWsBg4AX1NKPaCU+moyviAIwhEwEMSYxAYGEh9obZV3U/E4gxWcC7PRidpc6cAw3ScH+4buEQUDLvd98yRaFpY581U1z9J8uFSKbEbfdZSdrVKKku9m4kYdTYXs9fkKqajyCoJwJPRVoyGtvGlDrq5r5U00TXI+piZnFRMmY2Tjof9Qkad/sQi3oCkVnUwBHaCxaH92lcrWlpDOleZEjxL7rMIE+ptKYioI4ySKNX3VKFM9S9t5+6pW2CiIrTdpVzmgsy8gimvKaHu6Kzx+BEbHec5Z3s5nP+3x19/YyZXndTC3qUDJc5J5Jxsgip6TBY9iYhMjO/ZThgecC3zZGHMO0A/82eAXiQK5IIyO9eOz1QOwIxNhXJspTcWPtLEbg2m3CkBXf0BhnAIcD9+8gkp3gfPfsRmvYJPg81d21HWeKGqJqudYcQ+loOQ7WcU0beOFQYmpVEwFQTgC0k23lDTpHIzJte/WVUyNIYxMNmc/Hh695QSUMpz5ip1D4lWD7+I6tmMv7czzkg269LUF1yamEx3rJtvHVBCOCeKkVbe/att0tTH0VSOiWHOwr0oUGxa1lahEMdWkJcO2Wjy765a7CoQHW2hfWubU1QW0MXhJP7+hJn7kKIXOWcMoRZa0CpPOTmCnMeae5PGNDJOYJj7O1wGsX79ejGQFYRBppaC/miammjg2dIUBe7sriXE8yUy/rZBGsWFfT4XfPH1o3Nc59SU7mXdSD3NW9mfHlnXUxsIdRVYJsI9topkec3O+pSn5xVl+8SYIgjAWqf9oislZvuTXkYlJTObVbKjNlwax7S4ZT+RxOtvZ/rt5rLliF40dAb5r5/JT39J0Tt7PJaa+p3DdmjFMWiWdYBtTqZgKwliUg5goETEKkw9+rA09lZD9vVV+8MBuvnvfTqqRphrGVCPN/t4KP35kzxEpo1184lxedtYiVs9vtG1kCjZ+dxVvvboJQg/PsYqQrlI0FKzAUWPSeuHkduj9REFSlHenBmPMXmCHUmpNcuhy4LFpvCVBmJWkFgkDgZ0LDSKNwSTjEjUhkNQuphLaiulIc6SDCctWlK5pbsCKCw8yN2nF9QdVWj3XQeVm9NMKqMIu1jxH4XtWnTelbsZUfKIFQRgH6ax82pKbkiad9udBqrymVjHNK/iGsXWIGA+/u3kxhcaINVfsBuxmGpAlnZ5jPZvTdadKXCDy2iWpf+lEjy1IxVQ47om1GXV3uxymgcNks07pLNTOw2U6BwKMgX09FSphrb3sqQP9I55zMOev7GBRa4m1y9qY21ygsdDFr39aZMcDc/jLT4csnOtROaTQBpRj2yw8p7Zj7ygwdW1nsuc0xbwf+HaiyLsF+INpvh9BmHVoXbM7ADtjagxUojhr2TUGomSe6kgwGn7zlTUUW0IuetdmAJqKLueuWDgkMfWTqkDq0+clCzLHAUfbJNVNqgnDId0qgiCMh96qVf1OK6CQmzEdVvwoP4eae30ifDTeLr3L3rmdp59w6eiAaghL2m3F1FHWBcJR4DrJyFgyU++7TqI+noofSSuvIEwKkda4zshS19rYZNRNVMqCWDNQte26B/uq2SxATzmkEsZ1wWK8pAugjqYCrX0+K1va+MJ/Lmb5yQHve7+hHCmKvksYaRylmNtcoK8SZTNRVjWyJtQhTC3GmAeB9dN9H4Iwm4hiXbeJZoBKsrGnk01Ag60q5CumBupmqcazYf/E7Us48GQb63//qbrj+TnRFD+pmDb4DuUgpuQ5dlMQhVIGL2nrHWkDUGKwIAjjob8aQ0vN8iUlv3zMV1KNqXWW5K1jYlNrBR5t6Zmeqn2OZsGp/Sxrb2b9yg4aCh6H+oJkXMEqlBdcu/mmlGL5nEaCSOM5DgWvpm0CEx/vpKwiHPfkFzhmUDYZRDrxhrI79rE2VENNfxChDXSXw+yDblXVju4eUlWzkufQUvL41hfa6Trk8K6Pd1Is2N2qopfsXClFY8Grm29ycsppslsvCMJsoGdQC65JhI+ymanIjk50lcNs0RXF1q80PoJge2hLM4/+cDnLzjvIiotrwmNqhGksz7HPtDb4+J5DU9HLfEvHIywnMVgQhPGQCr2lAkaQzJHmKqb5UDe4fTd9Pk1Yx2LH7+Zxx/85k2bTzLnL2zlzaVude0OqumvHF5zMs3lec4E5TQVcR1HyXAqJAGdrgy+JqSBMNFHuUx8OUjQbCCKMgWpk/fMiralGcXK8XtwoiGww2d9TGZLgjoWjbL++5zg0+B7tczRveNcAJ58RZIshR6lM2AhqwkeQvNednN0rQRCEyaC7HNY9NiSbgIkab5jM9fdVomx2Kow1PeWoLm6PRlh2uff6k2lor3Lum7fUVVdHmgVNW9QafBc/Ud+14ke2nTeduRoJicGCIIxFENnRL0iKIqY+8TRZqjloxpTa8+mIWazHzkyjwOHh7y9HR4pFC2DF3KbMxcEmo1ajxGqWpIq86diCQ2PBtRt0SaGk4Dm0FD1cmTEVhIklv/MeaU0Bh0oYU/Jd9nRXaG/0qUaaxkJSMTWavmpMg1//8amEMRt3dbO3u8JZS9vGdW2FjSW24uniJr38b/3jHuY0FYAGCp6TKUL6nlPnIZUyWBFSEARhppMmpkGkbTxLZvVjY8XltCFbuMW65m16JPQfKmKM4oJ3Pkmhsd7ndLjKZiryEcRxtuHXXPLoLoe1iqk7uuCHJKaCIIxGFFtBzXTDLc0rsyqprv2czpA6jsqqqqmPqX3eDEpkh6KAzT9bTPlwkf/vrw6yfF4jPeUI13GypLSx4FHyXWKtUUrhJ8JHERrfdTBG42bVVJusponqRDLmClYp9brxHBOE2UqUW+iku/CHBwK0NhzuDwgjQzUR5Ahj66c3UI3Y0dlfNxPwu22d7O2uALCnuzyuazcUHJqLHm2NPo0Fl7/9jMPdv3JRyu5StTcUaCx4WRDwXYexYoAsikZHYpogTD7RGAlkkKiYgxU30touqw722tjbUw4Tv2idnM9k7zsS2pcN8JJPP8C8E/uGPFcYZka0mG4EopKfycYoUi0AV43UBGwZ/dnpR2KgIEwv5TAminXOi9Rk7bjp3Gi+XpoXParZxaQzpvViScNR7fXZdPtSlqw7xHkXxbV23GREwU26QPIden5SKPGcdO2p6kYa/ES1d6IrpuMprXx8nMcEYVaSbwlLFz97uit0l0MqUUw5jAlj28+fqvLu7a5w28N7eXJvb/bevmptXupgXzDmdRW2uvni0xdS8ByevL+R//0Zh7vu9CgkO1Elv+ZTWvSc7PhoyHzTmEhME4RJZqxO295KbW60EsSE2ipKRok6ZW/VjkvUKqZpK+/4Wnh795d49IfL0LHC8erfs7C1yJqFzaye35wds+q7ipLv2oVX0q6mkja2NPaqzNN05Dg7weu0yeCoY6BSql0pdaNSapNS6nGl1MUTfG+CcMyTWl0NVuKtS0BzljBxdtxkFdTsuWRdmp7rqf1DN+E2/2wJOlKsfdV2wMa65qKbxTLPyc+WJu27ySad59bGyNzc69KK6ZSp8iqlXgq8DFiqlPpC7qlWYHymYYIwgzHGoJSqEz+KYtvz31eJ6KtGBJEVOgpjnYlxhDqmP4iphHGdf95oYhxNRZdKqDl5QTOb9vaycm4jB3qrvOLsJQwEMcGAw1/9aQMnn2L4kz8L2dPvJIJHVnU3FT8yMOqCCBD/0hGQmCYIU4cebfse6M3NjfZVI1pKfl0rWiW0yufVpGJ6JMJycai456sn03+oxOrn7aOhvX6W9cylbbSWfLxEaR1sjF3YWiJOWubcZLwi9S313GT2SqsxfaIn2tdvopigGPjPwI+NMa9N7LEaJ/g2BeGYpxLaUYEsAcXUqqamVg3Nt/NCvqJq233tc4YgNugw4uYHdg17vXWv2sn80w/z4oubANstUnAdqpFtKXaSLpA0KVXKvsZVaZJqY172Goeki28KE1NgN3AfcBWwIXe8F/jwhN6FIEwRec/SSBt8V9X54VUTFcgg0lSSSulANc6Ol8OY/354DzsP21bdnV21lt3RFk5XnrEwa+86a2kbjUUXbeD0xS3cv72Lb/5LOzt3wJ2/MDQ2KopVuwgqFRJBI2V38vNzpcIRIzFNEKaIWJvMDiadj8rTH9SUda2ieX0rWpR0qeRbd1OF9MbC6PIYD31vJV07mnnO+x4fkpTmKfoOcWAT4DS+amOydrWCZxdmXrIAy9rcxliIzdC8FJ5lDFRKtQLPB94BYIwJgLHbgwRBqKMaxdnMqElVdZM2XUiVdmsqvXFioZUlrqRaR4ZIG8JIc6C3MuQ6xoCOFI1NhgVreljYsgSwVleeazfmHG2roioZU3ByVVHHIamoOomNzGDl3ilMTI0xDwEPKaVuBvqNMTGAUsoFihN6F4IwReQTU9ujH9ft7FcTe5gg1lSjRBGyaiumsTYc6K2yd5zzo3laS4W6BLjo2QFzz3HY/kSRn9zYzAc/ZHjuJYrd3SS9/05WMXUcu3BqKo7styqMjsQ0QZg6YmPoHAiY31wk1Jqi49JfjZKZJZVt9kEtMc3H4rSamk9MH97ZzZaD/fzeWYvrNuk27uzOft5+7zy2/GoRa67YxZKzurLjL1gzn3u2djIQxNlsadGzFQOM/XluU4HeakRjwc28q9M1lxUFMSg1dul2plZMJyAGrgYOAF9TSp2NTW4/aIzpz79IKXUNcA3A8uXLJ/A3EIRjgzA2FOLBrbvD276kbb5xqsSLjY/aGGINh/qrhLEeVvpo90NzeOh7K7nio5t4+XNbs447z03FjRRh0qKrkqQ0FThyVK2CWkiEN9NRhlRk03OcCY934ym/3A405B43AD+b0LsQhEkm3XnKt9vG2lgbgmTdo7XJPPSqUUw1srYFvZWQShhnM6RHor2hgJMWNNHR6FNwHRoLHkXPpZh4QHmu4rQzY/7+CxX+92dtUCi4Vobb91S2gEpbeWfqgmeWITFNECYZrQ1ak/k/g00y0znSMJeY9lfjTJUyJY5roiBgZ1K3HrL5z9aDfWw/VJcLARBVHB787krmndTDGVdtr3uuvbHAi09fyEvOWETJtxt8Rc/FVYr5LUWKvsv8liINvktTIbWHqY1OFLOF2di/+yyYpjjaGOgB5wJfNsacA/QDfzb4RcaY64wx640x6+fPnz8R9ysIxxTaWPtBMsGjWitvLfbVNFC0IRkpA3Izpuk61b6sPvDEoWLjTSvwijEt86r4OXss37Hrz3RmNPVuTiugThL73GSMAaCtwQfSrhF7HneKW3lTSsaYbJLWGNOnlJKZAmFWEWuTtC1owC5KIm2yOVJjDL3ViN5KhNZ2AdU1EBJpw/eTnv2zT2jjCJ0KUApOXdRKe2OBShTjOXZONF0Y9XTbdt33vMulMVkmFBO1tELSPgY2EChncNgRjhKJaYIwwQxu1410uqNvssVVugBLvaENdq6/HMaZj1/+/VATpLvzyQNZq+9je6zo3PK5TXVeqF5J87z3P06pLcTJNZect6IDsHG0oVB7It0cbEj8+XzXocF3aSy4OXVK+9qS7xLGZlx2XGPpAMwAjjYG7gR2GmPuSR7fyDCJqSAIYxMn4kd579K8Enn6mlTsKFZQTYonadJaTTb7DvZVGQjqx8Sf/sUi+g+UeO77H2PJnFKWYELqUWrtsFDxIGE3G8NSBfI08UzXra5DpsTrJRXViWQ8FdN+pdS56QOl1HnAkfcyCsI0ku065RLLWBsqoeZgXzUZJE8M3bUm1vaDDrVh8wd3dPPwru6hJx8FpWB+S5H2Rp+C6+I5isaCS4Pv8ts7ilx8domtTxTqbAvSpLTou1kgSXe1ZsGCZzYgMU0QJpjBYkfpIksbkyWXdtFlE9VKkowOhNYqRufmq4Bs9CFt6Y2GUeO984n9/M+m/RgDh7ZYhd2OFf00tNePPS5sHb5LNa0OFDyH+c1Fip5DU9Grta1RE5NLrbo895iIwUcVA40xe4EdSqk1yaHLgccm5xYF4dglVdrNWnmpVUjDbL1qrFp5Uk3VGg70VhMlXk0UG6qhff5Xmw/yyK6e7PyVHp/HblvGojMO85pXeFx5xqK666fxLO0ESWdI3eTnvPruYKcHR6n6tekES5+Mp2L6IeB7SqndyePFwBsm9jYEYXJJ10z5Oc9I2xnSINZ0l0OqUUwQabYetC1iR1odHY6FLSVOXdRCS8mjoeAQa9vmFfb5/N1flFix0nDqqbpusVP0bFJazM1QpTtSkpdOCB9CYpogTCh58bc4ad9NrQwirZPk0wrLeY4iiK1ZezmIs8qqGXQOsIuzkTg8YKulW+9awP3fPpFL/uhxFp/ZNeR1eV/R1ArGJCJHnqMy71LPTTtVasbx+TWZNZ0/JoLwhzj6GPh+4NuJIu8W4A8m/vYE4din3ovUtuam4ptA0lFSa+WNjWFfb5UG3+WJfb3c/fQh3v381TXLmdy5t/1mPnHocPbrtuF7LXUz+UqB7yqMoW6WtGYHk/MzTRLUPK6j6Gi0bb3eVIofpRhjfqeUOhVYg+0k3GSMGVnmThBmEFEyEJ628A6eMU0Fjg71VXEcRXc5rPM17RoYW3DQcxQXr55LVznEYHh0Vw8GK7axuK1EQ8HFdx0WtzWwt6eCMfBnHy7S36f4l68GNDfZ51McxxoX5xNTsYCZOCSmCcLEk6+Y9lWipEXNdqnEyc4/Bvsdx9qyuIr+akQUG/b1VIbE51ibMQc2D+9o5MEbVrHgtC4Wnd417Gvy66qi76BQhLHOqgJFrxaDHYe6eX43Vw5wHXVMjFM8mxhojHkQWD+JtycIxwVa14se2Yqppj/RMzEmWcMmbb5GG/qrESXP4aEdtnuvrxLlNvRq8XPNlbtZdGYXLQsrKFpy8cxu1HmuQxybOoVdhapLRlOV3sEFEUcpmhq85HxDE9dny5gF2GTu4GNY5bWHgZVKqZdP6F0IwiQRJe25aaE0zi2ebBtEnCnvHuqr8tieHqpRDFilyDueODDmNTqaCsxrKXLSgmZWz2vOPsTtjQWKvm3btYsfh5Ln8sMbivz0xy4f/Ysqp50GJX/ox7CjsVCXrAoTh8Q0QZh48tXOgTAiThQn46SVNxXoSBXO08XWQBATacPm/b0c7KttBB7orXLLQ7vZM4oKerXP4+5/XUOxOeTCP9iMGiFkpsumgmdnSG0bm90A9JJNwLRrxXMca4+QPa6vth4LrbwSAwVheklbeWvjC7WZ/IEgTo6YnPiRqSWyufPk51ExYDRUun2UgvZlA0BtXjRfBfUTe5jUAiZNQPMWMDUhpPqYlyr6wjQlpsDXsD5VFyePdwKfmdC7EIQJJlXhDWNNEOlh55T291bY21PhmYMDdPYH3PLQHjbu7OY3Tx/i5gd28cg450mbcmIaStXvzjf4LiXfCml4jkPJd9jyhMdlL9C845oIBUP698H2/xfFs3SykJgmCEdI2lIbjTDjYDDZc1Fs6KmEmZDH9s6BxM7APi6HcebNl6qdl4P686Yz/vt6hnrzgV2g3XP9yVS6C1z8nicptkTDvg5IhI0UJd/JxOWcxCDedRxaS3620EoLpKnIUV3niqotyGY5EgMFYRqpeZPWBOHSDbz+VMQoPZZ0j6Tr2F2Hy5lg0qC8lK13LeDHf3UOvXtL2fFUbTethDrKqvI6ieCRoiZ45DrUPExV7fV53NzG3XDPP1vGE2FPNMZ8DggBjDFlRBxUmOGEcS0RDeKaLUFqUgzQV43prUTc9fQhfvDgbjr77UJoywE7Y7qne/gFUZ6FrUXOWNKWPVbJl5f06Zdyu/OOA0Xf5bP/J+K7N8eUfCf74A+HCB1NGhLTBOEISXfu810neWJtanE3STi1qSmcl8M4eWzb0bS28bmvMlJCaT+SPeWoThsge1bByosOcO6btzBnZSYwy0Wr5wDw0jMXcUKHlTp3XStwVLPqUknF1MF3FC0lL1topfP8qSBdqkQJtWrDMYDEQEGYBtKiiUnVeKlVQa3wprXSArIxtLRdV2u74XfHpv30VW1VtascZOvbar/LIz9YTvvyfpoX1q9f8/6krqPwPSdXRa2NKaTdfWkrr+cMFd1MzwEkVdept4sJlFINJNVjpdSJQHVC70IQJpj0gxpqnUj8J8eNTVQLOJSDOGs/O9BbpanojnC2kTnnhA4KnoPr2J0r37NtYH4ixd2Y2BAAfOsbLmecbTjtDENTURGXa2powpQiMU0QjpD85t5wRLFBa01DwSWKdWJjYONt2rJbcB2CWGeWB8ZQqw6MwKH+gHu2dNYdCysOfkmz/IKDQ16/oKXEq85ZCsA5yzs4c2mb9YZOrGGKyeiEUtYn2vds5SCN06naZCoWUvTrZ0yPESQGCsI0kFoX2qSzpsqbjjdEWhMlnX6p53MQx1QT0bgozjf/wq+fPEhT0aZyD//gBMKyx7rXb63r3DPYDTfHsQmql3xlFdFcy67vOpR8G+uMqQnF5ckr9U7GZt14VsR/BfwYOEEp9W3g58D/mtC7EIQJJt1hD+N6I/dU8OjwQMBAENUFhZEWXKPRVLLJbLqI8V2763Ti/GYKnk1MAX7zG3j/Hymu+xePku9krWUou5svTCkS0wThCAmTmBqNECdDbRXO09cEkc4qptoYeitRNvM/kGwKpvOnYGPwnU/sZ1eXnSnNr3X299Zypu5djfzoL89l98Ptw95HfkHmOoqS72aWML5jFXfTakFqzQUMmelPY3p9xXSsf6VZg8RAQZgG8vFTa1PzMNV2FKK7bLv4bnt4D2Ec01MOqYQaqPeDTn/Q2M27wzsaeeoXCznx0r3ZbCnABavm0Nbg51pzbazzXadOhTcdQ0s7StxcN99wFdPMLmYSOvtGTEyVUpckP/4SeDXwDuA7wHpjzJ0TfieCcBQM3slJibJWXp2JbaTHq1FMdzmkayDMKqaGkSsBI6EUdDQU7ALHtYsXz3G4et1S3nj+choLLkXP5fBheNObYMUKxT9dayh6btKjbxdHDf6RV2qFI0dimiAcPXFqW5CzccnH3zCuGcPbDcA4mykF6C6H9FUiwqg2Y2pMTbxj68F+Dg+E3LvVVkeHC+1Bv8dvvrIGxzPMWd4/7H0Ot0zy0oQ06WRJKwMNvpttHg6uhmaJqZfXEJjdmanEQEGYXjLf0kQYLrWB0QZCbTjYV2XbIRvbqqHm33/7DN+5d3smFmfJBcfk2P7H2yk0h5z+8h3ZU64DS9vtOEPacusmHX1poprNlia+zalCeVpdHY58l99kjDaM1sr7BeA84G5jzLnAbRN+dUF4llQjXbejnZLuLIWxrZCmi4xyGNPZH3DHpv38z6YDLJ/TmL1nhBx3RM5b0UFLyaO7HGbnbyl5NJc8mkoeDb4V2XjXO2H3brjrLpjb4WQLoIJr2zgahlHlFSYFiWmCcJSkMTX9Xk3moBqSxC6MdFatrEYxA0GM1jbxtKIetqIaJJuF6T7gcPuBm/f1cqCvvrPUaLjn+pMYOFzgso88SqltqLvJ80+eV5c8eo4i0iYT9PCSSoGjYltB9RRtiR/fYAqDWnqPESQGCsI0oXOdeWkxxBi76We7RzSVsDZiVg5jDvcH2ePU95RcQSUNn2uu2M2Jz9uP1xBn11NYwbdKqGsKu47KYltN3Cixi1F21CGIk3XqCMWayR5pGC0xDZVSXwOWKaW+MPhJY8wHJu+2BGF85BPTKNZZe0FWIdV2ERQlokeVMCKMYjbt7QVge2et5SE6wsx0cVspMyW2QkbWImagGlkfUt/lR7d43HQTfO5zcMEFkN/P91xFFA9vFyNMChLTBOEoGTxjWo2smFGamEZao5QiiDS9lQhj7CIqiEwWg4PIzvyn81N5/NxIwyO7e4Zcf+PNK9j3WAfnvvlp5q7uG/I8wNzmYhJX7cl9T2Eig+fUxifSakHRtcfaGwrDnusYS0hTJAYKwjQQxXpIZ55OYmSodaZgHkQ1obf+IE6ST5O9V2uTr5dS6fXoOVigY3k/xaaoTqWXRIAz1iZpzbXJaBrbUvsrJ6maFn0HL0lcHaUwI+Sf05mYvhx4EfBCYMOk3oUgHAU6aRcDu+PdH8S0llSiDmk/nZXQLoTKQUxfNeTffr2N0xa3UA7jIefLW8kMx5L2Eru7akpn81vsIqjgObSUfJpLIU2J8Edjwc6SvvpViug6+MM/HHo+z1FoY2Z9e9gsQmKaIBwl0aAZ02qk6+algsjguWRiR2nrWaRtdTSNy3a8YmisHc232RhwXMOaF+5l9fP2j3qfBdchim18byp4hFFIQyJC57sOXpKQFjwnS1iPIyQGCsIEE+S68kZiILTib1nFNLF6sf6kJlnP2tiYtuzetnE3nuMkr7VCcnmvZ4AHb1rOM/fM42Wf3UBTe72Q3NymAo5SNBa8xMvU2sGksVYpRYPvZrOnbQ2+HXPw1LA2hinTmZj+qTHmY0qp5caYb0zqXQjCUWDVHVPfPE1/Nco853oqts2ruxziOYrth6q0N/hoY3hyXx/VYRLTkVDKBhGVq3ae0NHA3KZiIq3t0lLyWNhaorHoUQ5jVOQy0OPQsEDx7nePdF41KYPjwohITBOEoySrmMa2HS3IicpBmrg6ddVQbagTNxoIYsIR2sNG2hY0GpQDZ71yO0XPoTq6iG+y6EoS06IdtSgmyrupqEfBdYg9c7wlpSAxUBAmnDAePTGthDF9lYiOxkLObsvkOvtqMTWPNqCTWfwgsiMRT+zrzZLGA5tb2HrXAtZcsYtSa1TXhbJ6XhPrV3Ykm3Aqs4QZXDFt8N1szjT1Nk037EayBptsRkvxX6aU8oE3TtXNCMKRUA11NucUaUNnf5BYFMChvoDO/oB9PRUe2dXNdzfs4J6tnba9LOnrH43lcxqyn9MgUPBqi5jmZIbUcxwaCi7NRY8l7Q00JL6lf/Inildd0US5f/SFz3G4MJpOJKYJwlESa0MljImTlrN8ZwqQHdvROZD58sW6piQZJzE6iod6ksLwQnZ9B4r89DNn0/lMEzC8Ku7C1iKL20q0Ndh9di/XEuzn5kQLrq2QOsoKfBynNl0SAwVhgglHiGkph/oDDvRWiY2pq5imrbx2drTWjZLf2LOx1MbeWuw06Ehx/3+spmluhdN+b+eQa85tLtBS8nAdK+CWnyX1czOmXhIXFTWHCN9V01o4Ga1i+mPgINCklMoPfCjAGGNax3MBpZQL3AfsMsa8/KjvVBAGUY3jLCCkiemithKeo6hGmu2dAzy0o4sdnQMoBbu7y2gNWpkRd+dTtneWs5nRV52zhLu3HGL9ig5Oml+hEmmWz2mkpeTRH0S0lDzaG31bKQB+dpvPv/4rvO+DMW1twwtrpEgb75QyITFNEI5HtLGtZqm1VuoJnRLGBkdp9nRX8F2n1sqbU+qFkUcmBuelwYDLXV86lWqvT6HRlkmHM3I/aUEzC1pK2WxpKuzRWHCzeVLftbNTqc+0Ffg4LmOvxEBBmGAGVzoHE0Y6S0rz4kdW+6TmZxpE2s525t5rkuQ0iONsftQAT/x0Cb17G3nuHz2OVxj++q7jZMlo5leq6sWP7Ovs82kRJl2XDhdvp4IRtwyNMX9qjGkDbjPGtOa+Wo4weH0QePxZ36kgDKIa6lqbWGzoq0bWC0obyqH1f9rbXbEy3LFhX08lq7COZDOT56qzl/D8k+fRWvK58vRFtDcWWNTeYBdCrUX8ZEZpUWuJoudS8l22PeXw5x8uceGF8Fefmp42CGF4JjCmCcJxR6yhGsYYY7LWs/ysaCpwNBDE2YyU1iZr3c23raU8c6g/W9Tlo6WO4Z6vnkLf/hIXX/MkzfOtQu9ga4KTk6QUap0tacW0rcHPZkqbi15mF+MkXn1F7/iz6ZIYKAgTT3VQYjrYejCMdWavpXMvTYWMYm03/G7duIe7njpYt0mX2WpFpm5Tz3ENyy84wKIzu3Kvzb3P2N2mVInX2sUkLbtemoDa1xYST9PBiehkWMGMh9F8TE8FMMZcrZQqDnruovGcXCm1DPg94KvP5iYFYTiCWGem76G2i6LYGAaCiGoYUwnjZIffJrCH+0MM0FeNCcYQOlq/oh2lrDl70Xdoa/Rxk0Fx33VY0FLET+S30379sOrwljc7FArw3e9CS+Pxt/CZyUxETBOE45VYG/bn2tFs626tGhppQzXU2SIsPR4PEk1K6SmH3L+9iw3bDwP1m4Ubb1zJvsfbOffNW5l/Sg9zm6xybn7hdOkp8zlzaVv2ODWKT9vPvKRaWnCtJUy6kZjqAqT+pccTEgMFYeJJO0fCQd0h6fcgscey7bppcaRm/xLlKqmH+oM6L+Z0LCKKTfZesPYw57/jqfobyYVYK6xJosZbq5Y6iiEVU9+zietogkdTyWhDFv+R+/nuQc99aZznvxb4X8DodW5BOArCWNvd+1hnC6BYG/oqdgg8nTmNtf28Dt7VGo55zQWuXreEtcvaAWuFUHBdq1zmKJqy9rB6dUeASgWWLlFc97WI5cvJhJiEGcNExDRBOC4YPAuqjaG3EqE1me1LuoOfJqN91dAuuJIk086jDr8JmC7iesshWw/21+aqYkXfwRInX76bVZdYBd6GxBIsP5M/p6ne5sXLxeT0cWrb5TtOYoFAkpg6NBVHm2Q6ZpEYKAgTTBoHDw9Yxdy0YyROqqRhXGvjTZ8LkvZeYwxhpLnr6YPZ+fKFyzQuhloTxIbdGzvY+UDHkNcNRhsyUSMnVy11kpEGqM3s+66DQk1bhXQwo0VmNcLPwz0e+malXg7sN8ZsUEpdNsrrrgGuAVi+fPlYpxWOcyphTMl3MckiCexOfJS0lWkNXeWQchAxENa3iHWXhxqye45ixdxGnj7QD5C1ey1ub+DwQEiD79bZCjT4LlQjXKXsjnyuX3/OHPjRj6C7bK84Uz7kQsazimmCcDyQ+kHHxmQLBJOrkuqkKyWMdSaElMbgSjhonjRXCRhMmrz2BzEP7ujixPlNmS3Mc967qW7Rlf6cVlVbG4YuXTxX4WtrjQBWwMNzHIqejd0l38n+FjiOouQcfxVTJAYKwoSTbrKlYwmp32gYaxzlZAmpFTqC/mpEOYyzzpKecsQzhway8ylFtnBNY1akDT1dig3fXk1je8Cisw6jBtU+6lt5TeYk4eaqpc4wM6Z+Igo3GyqmZoSfh3s8HJcAVymltgH/CbxQKfWtIRcx5jpjzHpjzPr58+eP47TCsY4eYSEDNRuY3mrEgd4qWluP0ijZsY+05raNe7jt4b0jqj/m8V0nq46CFcxQKOY2FVDK2g1YqwGyNl4/eew7Dq6reHSjwxVXwN69NqBIQjpjebYxTRCOeaJBbWj5nf5aYhoTRppyEBNEmu5yiM7ZHaRCHkGoh4gapQwWDNnySAN3/sMZVLp9HJe6RVdLyaep4HLmkjYuP3UBzztp6FrBS2wOSr5DW4OP5zoUkllS37Xtu2lb23GMxEBBmGDSWJl2h0SJJ+nhgYC4TnnXxtB0DCJt7x0I661ehhPFjGLD9f/YRtDnsf5tTw9JSgejkyHTbHY0WZt6Tk3kKF2r+l5NsXcmMFrFdJlS6gvYXbT0Z5LHS8c6sTHm48DHAZKK6UeNMW99VncrHPNEyS58QTnZh1Nrk7Uk9JQjFrRA90CYLJJshTRKfg5izZP7ejHYnfixaCq6dRYERc/JElLXUSztaEBhP9gdTQV6yiF+0i7muYpKn8PrX68IAnCTDfjjfOEzk3lWMU0QZjtamzE3zsJYU/LdLEENY229nCFT5a3E1qqrrxoRRJquZAFWS2rhtof3sKS9xIWr5tadP4o1nf0Bv9t2ODvWu7fEnf+wCr8pRLlD8yPXUVxxxqJR7zuNyb7rML+liJeMWZR8+7ek4KmsanEcIzFQECaYKJu115klYaistonWNu6myWmU++rsD9hxeIALVs4Z8xq/vtPjzlubOPUlO2lfOjDiLtLC1iL7eqoYDAqy9WveLsZz67NaPxE/mg2J6Z/mfr5v0HODHwvChJAasnsOpHZ06WB5yXfZ11PhpAXNVKOYKLb9+Z39ASXfftBu+N2ObNdqwzOHh71GntMWt9JS8kk7J3zPimM0Flw8x84ipeuYjgaf/mqEl4geuUrxwf/PZ/t2+OUvIS34T5f3kzAmEtOE45rYGJwxOjbzSudgE1PPcTDGUI2s4m5/EFnxudhaxnSXw7qW3bTrZU9XZcj5793Wyb6eava40uPz6/97GijDc//4cZ5zRsu4Yvdgip6bxfCmopu077p16ruyaSgxUBAmGjtvrzMHiNgYTGwT1iirjJpE8EhzsK9KrA2/ePIAACvnNtadb3AxNKo6XPe/O1iyIuC0lw31LM2zal4TRc/h1EWtuE7iR5pr5R0u+Ux9TWeKLMqIiakx5hsTdRFjzJ3AnRN1PuHYJd1RsrNE9gNkhTVsYpoOl1dCu3O/eX8f1TCmqxzSUvJ5Ym/vuK919bolWRJ62uIWHtvTm9tld2kseBQ8J1uoFROT4nS29O/+TnHbrYprr4WLL66dV9Y+M5OJjGmCMBsZT7UwHKSiG8YGpWoepEFkGKjGGKzgXBBp+qtx3bnT+dHhrnawr5aURlWHu750KpUen0s/8hjN86ssbhtaPRjL3stRdu7UDBh812oBaJPawjh1rzue66USAwVhYqmEtkhSiezYQpSIcGpl50nDuDZbGsSaSqjpKVfqrF9+mSSoKZ7rEMS1jj+3oFl79Q6ec06J7b4ZNYb5jsN5K+ZQ8l08J7WBqVVNR5sjHa6FeDo4LmXphJlLnPXi28dRbBc+kTa04RMmj6vJ151P7KevGtNYcBgINEfSiWDbGuwO0uL2Eo/v6cVNLGIAFrQWKbgOcRxn7W+eozBGEVQVX/86vPnN8IEPDD2vIAjCTEOPMXZvVSRtxbOmKKlxHYU2tXnTchjjOYpYQzmMGQgi8iP9+erp0wf6OHF+MwBP7uute11Y9ohDhwvftZk5K/uAo9vYcx1FU8GjHMQUXIei76K1wXcdir5T9zojHgGCIBwhxpgsccv/3FuJiLSmsy9IZkhtccVRNklNu0xibTf0Dg8ERHG9KNyQYW9jmNdc4GBfgNF23n7lRQfYfgSxKx1tADtLqhxm1BzpaEhiKswoYp22RNiPajWxJaipnWF9SqM4s4kBMjXIUXSThlD0nMTbTlHy3GyO1U8+zCd0NOK7DlVH15m3a6NoalLccw8Ui0MXUrPhgy8IwvHHWBXTVKCjP7CLrTC2ianv2pGGtCXNxmgb5w73B1RDXafpmr/Oxp3dzG8p0lTweHR3D0CWHDa0B7zozx8iL5CrUJy2qIXHc90vY4V13635k1pldZ15IXhOLTFNrRMEQRCOhGqks6JFGBsKng0klTBORhpijLGiblobNNbqpRrWWnn7g8jaGQIHcuMMg8NyOdT4riYOFXf+w5mc9MI9rLroIEdCKnIUa5PzMq217c5kxrxDpdQl4zkmCBOBzqxf7Ifbfug1lShOLAs0e3sqVMKYaqyz+dMjSUhTGgouLSUPx1GcsqiZpqLLpWvmZVLafmLQns6UAlT6HP7574tEgaKjAxobh55XFj4zG4lpwvHKWImpFQeC/mqcVUfT3f3+alT3mrx3X6RNvbn7oJ39nz++n98kPn3GwMabVnDP9SejYxjs2qIUNJeG3zNvKAy/ZLGzVFD0HXzXyaqmMDQeS3yWGCgIR0qQa/XIVzutIJxNPg02Tsbazt6n6ryp+FF/Nco6T3711OiJZk8l4rHblnF4ezOl1nBEdfPhsEJGTiZq5OSsYo6JxBT44jiPCcKojGYDkxIbuxCqRprtnQP0JR/yQ30BQayJta2OPrC9i58/vr+uLWy8zGn0+b2zFtFYcGlr8JnXXKC1VOCV5yxj1bzmzHw4Fc3wkgRVa3j/ewv8y7Uejzw88/v0hRGRmCYcl4wVgo0ha9+NtEmUdm0HS1fZzvfnZ6PAevBBba7UXmfohQ722fdv+vFSNv98CaXWcFjLg+GipzE2oWxKPEodlVh7Jd8XtBRtYupZH2rfrc2WDlYhlvgMSAwUhFEZPNeet7fKJ6nVKE50UWyc6qvabpM0Qc238vZWbKzsS77XrjX0+t07m3jyp0tZ+Zz9LDyte8yukXy+WV9QsZXSdMY07QicyYzYyquUuhh4DjBfKfWR3FOtwHHpTC08O/QoipBpz77WhkoU47qKx/f0sHxuIyXfpbsccChZ2OztrrDlQP+4rrl6XhNbDtrXple+5OR5tJS8RODIpaXkE+sQR0HBsx54QLaw8ZNWsL/+a/jxfzt89nMhF10oH4HZhsQ04XhnrM3BVDkyTMYk4mReylZP7WuiQeXQVCQpv7gaaXf/6V8s5NFblrP8ggOc/dptw86TDp84GlsNTZNNpWgqeoSxpqXksbC1ZBddiTWM56rMXF5m/mtIDBSE8ZFv3QXbspsSJR19jqOohHE2AmGMHTWLtEs5iPFcRTUyWStvmtyOZSAch4p7rj+JYkvI2ldvG9f9djQWss0/z1HJSIPKbGBSESR/pkjvjsJod1gAmrHJa0vuqwd47eTfmnCsEY/Si5BavITa0FMOCSLNQBBnHlCRNuzvtT35hweCMec4z1raxqvOWcrc5kL2gVQ5s+GS5yaKjW72s6MUBddhXnMRqO20O47illsUn/40/P7bNH/0x4yqbCbMWCSmCcc1o7XyHuyr5iwNbOdKpHXWypsmtWE89BxP7O3h5gd2ESXm8VsPDt043HHfXB64YRWLz+oc0SD+5WsX1z0uJYuouc1FO0OalAXSmO65ioaCS2PRTWZM7fOFxGsaxL5rEBIDBWEMtDZ2bh6SETJDJbSxLUq6ScLEszSIEsG4xCM5jA0D1SgRhDPJjGkaV5Pzj9GXu/fRdnr2NLL+95+m0BSP+trhcJ10DM3Gw3TGVCVr3JnOaHYxvwB+oZT6ujHmmSm8J+EYJb/RngpqpASxpuA5BJH1xGsueUTa/uw4vlU0C5IZJ23q2iqGI584OgqWdTSwo7OM69gPaGPRw3et+XrRt4bsao8dDE8KphnVKvzxH8P558OXvgS+74xpUi/MPCSmCcc7+YJpuuOfUo1sTLbtuyazPUirAenG4uCKKcAT+6yi7g837hnx2qW2gMVnHuaid23GcYcuzF525qIh809zmgusXzGnzo8P8tYHDo0Fj8aCR381yp5XSmU+2JKX1pAYKAhj01MJ6Q9iWhs8gsSu0ApuGgwm6ygJscrlaeJpsAWY/sC+tsG3rbzGmPoxijH6cpeuO8zvffpBGhaUj+r+PcfJNFIUtriSWsXMhqLKeFR5i0qp64CV+dcbY144WTclHJuku0TGWNnstkYnS1DDSEPR9vEf6K0yt7mYzJPGbN5Xpb8a0Zy0bu3trnCoPxj1Wp5b66W3XqUeHY0F+hIBj5aSh+soFrWWbOua0tnCp+TXL46KRfjJT6C9HZqbZv5ukzAmEtOE45L8Tn2kDb6qtc7GiUl8qopejXQi4GG7V9K3DlcxHW2pU+n2KbWFzD+5l/knPzHi64q5trm0DdetS0ZhTlOBZw4N4Dg24fQcRVPBxXNqLWuDkVbeYZEYKAgjMBDEdA+ELGwpEkQ6mRXV2WZdFNvKqMJu4qUWhkolHX6JSm9s7Jo1raimjJSXRhWHnr0NzFnZT8viCtEoxdIXn76Qnz62D4CFrcU6QSZrFZOKHyXV0tzxmc54EtPvAV8BvgoceU1ZOO4YvBOfHc923A0DYUQbfpZwpv6ldlCcrDoaxJqb7t8JwGvXn8Cto+zI5/Ech4ZE3KjgOZy6qIUzlrRScB3KoWZ+c5FKGKNUsqAxKttZTxdq5TLcdJP1Kj3jjGf7ryLMICSmCcc8UayHLELyi6M03jb4LtpAqDUDga0WFD2Xvd0VWkoeUVwT7dDG0FeJ62avRuPQlmZ+9cXTOPt121j1nANjvyFhfkuR5qLH2cvbs2OOUjSXPNoafSqhvYcwNjQU3KyCOlwSOgsKBNOBxEBBGES6dtXGcKi/SmyaEl9STTXU2c9BMmMaGNveqw30VsJEr8R2l4RRomquDY5SdWJKg4WVUjbevIKtv17AS//mAVrmhaPea3Oxlr41FTx6KrXXp+JHqY+pqxSOMzs8TGF8iWlkjPnypN+JcMygjUGZWoKXLpDSDjBrPWD/Fg4EMUXPTeZI7YfcviedZ9KZYXElGP/fz6Ln0OBbYaP/79LVzG0u8ciubuY0Fdh5uEzRdzLRDmDIokZreMc74Hvfg9NPh3POeTb/IsIMQ2KacMwTaUMiLJ51pqR+z2Bb0zr7Axa2ljLVyP444lBfQGuDTzmM6SnXlCUBHtrRxbZDA7x87eK6tlszTA2g85kmfvXF0yi1hCw6vWvYe0xN5C9cNafueMFzePHpC5nfUuRAoi2ggAbfpangobWhrcHP2ndT7YDh2tRmy2JsipEYKAiDiLSh4CiMSWbuNZRD24pr1XdtghpEtqsktTaEdM1qk1KTPKcTlwnPrR+jGC4v3ftYG1t+uYiTL99N45zgiOxhHEexrKORg30BTUUPhR1L87JWXvu6xsLs0DcbT033h0qp9ymlFiul5qRfk35nwqzFUP8hTBPAuoppUhEth7HdfTKw8/AA/3LH02x4ppONO7sAMsWz9LXjwVHQ1ujT4LsUPIfGgp+17vqukyk21s2hDuq9/9Sn4Lvfhb/7O0lKj0EkpgnHPGHO0iD9uRzE1lNPG8phzEAQW0/oyG4K9ldjugbC7HU95ZAoeS3Avp4KMNQyZvAiqmtHI7/6wukUmyKe/6HHaGgfffd/JEGOxoKbjWQopSj5Lp5rv7c2+NmGosLG/eGSULGHGRaJgYKQI/UaTamEmtgYugesGGcY2yRz097erLMviG0LL5BVTvPn6S6HxDkrmexagzbyggGXDf9+Ii2LBjjzqh3AmGOoAJx9QhtgReJWzm3k6nVLaEhstDzXtu3mRxwaZkliOp6K6duT73+aO2aA1RN/O8KxgDHJB0/bD0SaWMbGLojyFdNqonQWG8PGHd2EsWZ7Z5m9PRXWndDBvVs7s/OO1cZbTMSTGgsuaxa1ALb3vug7iQKvHQhv8N3MGiZPupv07W/D3/wNvPOd8Kd/OuQywuxHYppwzJOfOQqT+dHUUy+MbatZNYrpr8Y4TtrJEiWm8Hbh1R9EOI5HOetWGcHuK/dz0O/yqy+cjleMef6HH6Nxzsh6AIvbGjjYF9BYHH7BZAXqXCId4Sgo+TZRNcn3vIG8Sr6EcSExUBASeishfdWI1pJVvkwTSes9GmLwiBOF8oEgosF3iZPZ0ryjRBSbZCzNnrccaDxHESezqWmCOHgj76HvrqTSU+AF73kYt5C8eRyZ6cq5TWBg5bwmG/+S40pZm0NbMa11Axa9YyQxNcasmoobEY4dDHaHKErmf/IV0zD5gJbDiD3dZYJE3awaWpPi1JIg1oZHd3WP+5quU/NqOmVhC3ObCvRWIha2lHBdRcFzKPourqNoLKTWAvWLmKaix9698O53w6WXwpe/LIqOxyIS04TjgWBQxbQ/SOf7bTU0SMYkeiohjUmc7i7bymZaBSgHMZ7jZI/TeDh4Rir/sNAUc+arnmH+Sb00za2OeH/nLW/nhDmNrJjbmLUFe66qq8am1dFIa5a0NwDgOg4l3+A5VlXdUTWPPmF8SAwUhBrlMKavEtGUzG2mownWDSLGc53MIiZOOkh2Hh6gpeRn3ShhpBkIoqxqmqKNYeOuHh7e2cMVpy9k095eFreVsueNgZbFZU77vR3MWVmz2TpzaRsPj7EGdpRi9fzm7LFS9nyKxMs5sUicbeMMY7byKqUalVJ/mSi4oZQ6WSn18sm/NWG2ku42pQujdOdeazJvvDA2dPYH1hdKW3GNtHKavrb/CGZKPcfJdoXOXd5BQ8Gl6Du2hVcpiolXqec4mRn7cBXTRYvgv/7LfhUKE/GvIcw0JKYJxyL5xRDUt9v2lEMGgogw0vRWoqx1N4ptlbQa2bmp/kS1PE1EtSE7lic2hod2dvGDB3dlx3r3ljiw2XaqrHrOAZoXVEa93+Vz7S5/flZ1UWup7jW+41BI4nZb4uPlObWW3qLnZtVSUd8dPxIDBaFGNbQtuWkMTTfaUs/SVJk39XSuhDGH+oNk/jR5baZeXrPWAhtDtx0cAOCB7YfZ3jnAQzu6sueVglOv3M3pL6vF0gbf4aQFzbzsrEVH9HvkE1DPrcXE2eblPJ4Z068BAfCc5PFO4DOTdkfCrCOKh/raaVPzGk1979Jhcdtzb3fng0gTx4bDA0HmFwXj66+H2i65NRS2H/KGgsucpgJFz6XkuzhJNbXo2UXOso4GlFPf1rB/P2z4jc1EX/pSmDv36P4thFmBxDThmCM2Iyem+3qqdt7J2Na0chAnm4J2MWXnS6NMGyD/3rxIXLq+CSLNlgP92et79jRw5+fP4L5vnoSOj34RNLe5gOvYsQwg0QOwokdpAuu7jp09dawHdf5vgDBuJAYKQkI1irMZUajXQ0nbcO0IRJKgak0ljNl1uOYzGmsya5jBrbrpnHwaSyuRxmi4+7pT2PNwe91rHQXPO3k+MLT1tsEfPWXLdFJUfbFmOJeMmcx4EtMTjTGfA0IAY0yZ0W3LhOOMaNBOfdqbH8Y622kCkh0nnQ2DB4lPXhBrth3sp2sgHHdCCvYD3OC7XHLS3Myf6ep1S2zbrufSWHAp+Xa3HUiOO7SWfIqeky1k+vvh5S+Hd73Np7NztCsKxwiTEtOUUq5S6gGl1K3P9lyCcKSkcTbdKAx1rQIQxprD/WGy26/Z21PJKqblMKYS6szjeTTSD0m+xWzbZo9f/NMZKOCS923CcceO4ufmbGDyeI5DY8HL5v3TGdKGgptZH/iuyh6XPDfzPJWK6REh6zpBgERx165VoyRmphEs1nZeNErmS63Srt24q4Y6K7qkhIkHdIo2NqFN16D5tfKmnyxl1wNzqfb5dedY2FrKWooBrjx9IetOaAego6nAqnmNI46YpWtaRW1TbzaGxfGIHwVKqQaSIpZS6kRg5MER4bgjjHWdr11qzh4l80thVGuPSCum2lhBDm0Mu7rK3Pbw3iO+rpv4MnU0FnAU+I6iqehnO+uNBRelVNbGkLZ9OY6i6Nj7jSJ4/ethwwa44XuaOXNmx3C48KyYrJj2QeBxoHUCziUIR0S68ZfaxESxjcMNBRdDbdEURJp9PRXaGvzM7qC/GuUEjkbDxtKB5LWHtzfxv/9lIY5n1XdbFo7evpsyt7k4/NmTzcZ0cee7Nn53NBXwk8VdybedMJ6jaCq6mdKkVEyPCFnXCcc9OvEZzZJObUfQ0twyHS9LK6aVRJ1cJwJH3qCiTKxBKXtsoBrxk8f2MafRp5J0D6bnPbS1mcduPYFl5x1kxUX1/s6Dt/Uaix7zkjeumtfEgpYS607oGPb3sRt5sR2RcBycRPxotjGexPSvgB8DJyilvg1cArxjMm9KmF0Mtg4AO6OUfpDLidhG2sqb+jul3niH+kZWbRyOkxY001ryeGx3T2bz4ihFUzJPmloPNBXs/96ph1NHo58tqMAGife+F/77v+ErX4FXXHU0v70wC5nwmKaUWgb8HvBZ4CPP9gYF4UiJtMFN4ivYRVU5jG1iampqkXaWNE7mMm2s7ioH6DEKnds7B7KqbDqDuvWuBRQbDBf80aM0zx9/XjPSUilV3g2TSoTv2s6WZR0NWUW05Lu2UqrsxmSqFSCJ6REh6zrhuCfO/ElNloTa2Fbr8ks9SWNdW+um02v5WJsWRNLX3P74PgA6B2pWWcYYworDvdefTEN7wLlv3jKkormgZeimXUvJ51XnLB31d0lFjtzEuzSNh7MxLo5HlfenSqn7gYuwf08+aIw5OOl3JswawkHtDNoYykGMoyCITCK5HREbg4o13QMhj+3pYWVOjXG8rJjTyFlL2yj5Dk8d6Mt87N55ySpWzW/kwR3dWcuXM+iD2d5YoBLVdvR/+EP4t3+Dv/xLeM97QOsjuxdhdjJJMe1a4H8BLc/yPIIAUGcvkP95JFKl3bR9N/WLntNUwCQdKiaxgAGohjEFz7HVgmE2F/N0l0M2PHM4e2w0KAfWvX4rJzR0sSs4smJbvsOmHkVDwcFL5lQ9V+F7Ds1FL0uGS74jyeizRNZ1gkA22hCnnXyJVVZN0Kh2HKhr003fv/PwAL/bdpgXnrogE2iDoXOmYAU9t9+zkP5DRS798KMUGuu7VF525qIhopzjoanoEsUGLynUQC02zsYQOZ6KKcBSwE1e/3ylFMaYmybvtoSZitZmyCB1FNtdpdRDzgCVMKbkO3T2WyuC323r5Mb7d/L7F63g3+9+hp5KSDXUNPgOO3ID5MOhqLU3dDTZD35ryeeV65ZyoLeK59pWr9ZSIfG5q/9gZ8pkjqpTJ3vFK+Dmm+Hqq5PXzcZPsHC0TFhMS9Qs9xtjNiilLhvlddcA1wAsX778aC4lHEfkk9FIa1xn9DGD2Bii0GQ78KnvHqStZ/bn/kqUnT8cIyFN6eyvdbXsf7KVjTeu4JL3baKhPWRXcHiUdw7lFWsXj5hQOgoKrovn2PsquA6+49Sp95ZygiCSmD4rZF0nHNeknXtpN582xq4pkza7NGFNK6PxoLYSY2B3ly129FZC2hp8fv3UQVbMaRzxmqufv485q3vpOGGg7rjrQHGEDTtHMWpHS2uDT18lwks6TPIWMbPR23nMxFQpdT2wFngUSEtjBpAAdhwSxJrSoAVSGGsqoaaQCAqZpE+/4Dns6S6zel4z928/TBRr7nrqILGxO/djeTSlWPN3m6C2JjtSJd+lpeSxen4TWw/24zr2dUXPyVp5U7zc4qXgOdx6K5x0Epx6Krzylc/qn0OYhUxCTLsEuEop9TKgBLQqpb5ljHlr/kXGmOuA6wDWr19/JDpfwnFIfnd+8IJoONJFVLoOiZIZfiCpmGpcR2XJqDY2eR2JQ31V5jQVqISaBxN7g90PdfDbr55C8/zxzZLm6Wj0ee5J8/By8Tn13as9VsnfEasRUPRcfG9QBSAXz0Xw6OiQdZ1wPJO23gaRphzYUbPUCqZ7IGROs3VoSKunca5ldzCpAJLrKEyS2B7oHdpB0negiIkVLYsqWVK6al4jq+c38/PH92fJsOvUWoVTCp5DJRw5VvuOg+/aMTZ30FzpbNy7G0/F9CJjzOmTfifCrGCw0FF6rBzGOA5UQpMpm2lj6BoI2bizi4EgznbotTZDrA2GY8XcRp45NICjFDF2wbV8TiP91ThbvDhK0Vz0cB0n8St1hrRC5HfV7/qFy2tfAy95CfzgBxPzbyLMOiY0phljPg58HCCpmH50cFIqCEdKPhkdrHyexxgrJhdEiVVBbj4qfZsVPxp6jmHWWQDs761w11OHOGNJa2YG/8xv53Hfv59E+/I+nvtHmyg2j63im+K5isvWLBh6PEmU04qAo+zmYqao7qpswTbseWfjqmtmIOs64bjEtuvaxLQa2bWrNjbBjLWhmhM/qkZ27jSNv125edGUfT02Ce2vxnXjDnniUPHb/3cKQb/PSz79AI5nz3fSghaaCi6r5jWxal4TYJPQclAfmIdLTFOf0iDS+Imns3WhUHVzq7Oxq2Q8zcx3K6UkgAlAOqdUW+AYYwgiazkQ69p3bUBruGdrJz9+dC89FWtVsOVAX53q2WikH6izlrYBdtHiOiqbMXIdayXQUvIyAaSi59Jcqt9vSVsZfvtbeNPrHdasga9/fWL+PYRZicQ0YcZTZ9I+SmLaU46sNVesCXXNoivStVitjVXsHTwjNZi+asSe7nKm0NubtP1u/91cfveNk5l/cjeXfvCxEZPSlXOHb2FrLflDjjmKbBOxvdFWKBSKou/Q2uAn4kwq8wAcjtm46JohSAwUjkvinLpuNdSZUGeqzJtu8IFNTI2hbm4/T76C+vCu7hHH0jbetIKuHc2se/1WHM+wsNUKHBVcO6aw7oT2bD51ON2VYt34gp0pbS15Ob9nh5JvFcpdp744Mxu7SsZTMf0GNojtxcqJK8AYY9ZO6p0JM5Iwth/ktGoaxlbNrBzENBc9tDbs7Bpgf2+FPd22rcF3VVYtNUYPEUsazEWr5/DbLZ2cOL+ZVXObOHlhMw/t7EIp26Iwv6WI7yqMSRYynovrWBuYkm+FMgbz8MPwspfBokWK22+HjuHVtoXjg0mLacaYO4E7n+15BEGPs2J6oK/C4rYGgkjjKJXE6JphvG1Hq20WjsZPH7NKkqnPqNaGgSBm4WndnPyi3Zx51XZcf+R7GW5RdclJc2lvKNQdS71J7etjGgsunf32eNFzKXqO9eAb4Zy188y+RdcMQdZ1wnFJrE0mpBbEMWGamCYV0zDWWddJNUlgR4q/aYI7Gjvvn8PTdy7m5Mt3s+TswyxuK3HhqjloM3RjTSnr5Zxu2qVV0nyi6Tl2467oOvQHMb2ViIJr37OotcSe7krdHP5s1E4ZT2J6PfD7wMPUZhGE45QgsjvzBdd6JEVaJzOmtlLaORDwtV9voz9ny5K2pOWHyEejvaHAq85Zipf0zPuuQ8lzibRt+Wrw7cIl1nb3yHXc7AOeetoN5jOfgYYG+NnPYNGiCfiHEGYzEtOEGU++lVcnypDDLTK6yyHzm0uEsU1M+6sR1dBWTsNYc7g/pL9q4/F4RihSjIZf/KCVVZfsp9hsOPs1z4zrfWcsaWVfT4WDiQ3YgpbSkNe4StHgu1limSafjmPHMdJZU8dRmcq6MKFIDBSOS4yBMElMq8l6NvUlDWJdVzEtj5F4VkeZ+wToP1jkvn8/8f9n763jJL3OK+Fz4YWiZhie0UgjZksGmWTLIGPsbJINZ0NO7Hxh+IIbb9ihXWeT7BfHDseQbBzHFCeOHbMt2QKLWcPQ3F300r33++Pe+9Zb1dXd1QPSwD2/32i6u7BLU0/d8zzPOQeje+q45k0HAQDP2T2qo676lDVGtKtuLfQwXPJwcEFrUb1C3edMy9cCTpGazwiPE3hg4IwiYDTX5QPnr8b0oFLqw2f8mTicE0iEdnpsJBmqPkcqFJqJADMkdamV5uQzM2sOUgHHl6NVwcH9QElHI+UxYlYTCN72skswW4/ylQWfMYDpFTXPmC4BmrT2w1/+JXDsGLBnzym/BA7nPlxNczjr0bXKq4AoEyj7vMsBHTBbK1LmuqlWIvLV3uPLEeYacT4hWG8luIgkJrjjL/bh8F0T8EoCu547WJIIIcCl0zVcOl3Dv9x7ZE0nSUYJKgHPV+HsRICgEyVja3nA1ncjdjgpuBrocEFCKh2rBWhimgqVWwW1E4Ek60ge1nMtF1Lh7kPrO5KHwwkueuEMLnnZcVCu8Kbrt6275WHddEfLXj5k4ZSAM2qmqQQjZR8Vn8PnBI0oAyHaqVwohZLH4HPatWVS9gcNXzl7MMgzfoQQ8l4AH4Fe+QAAZyt+AUKZPXypFJZben1ASK1dso6QcSaRSakD3WX3wWoQ7B4vYyjkKPlcr3t5LDc4asYcPqcIPYrQ1+679SjrIqNFY6aZGeAXfgH4wz8EhoeBiy8+bS+Fw7kNV9McznoU125tNnTZ54hSmR9a4kzoRmGkp6QEwEqkm4NaO5V2GWk8PdfESNnvytvrRdLk+OOf24a5J4ZwzTfu7yKll2+p4ZHj9TVve8lUNf/69dduXTMflTOCHaMlHF5sg1HkTurFmAN7oFrH+8jh5OFqoMMFCaG0jhTQk1MhZa7DbMQZpAIOLWitaB6xFWf4zKMzeOllU7lU7Om5Zr6J0vdxUgLmKVz3TZ1Nk42kB4wSMKoHMrWQ5yu92ugTGKv42DFayuslowRln+nhjIkXq4a8K5livVp/tmKQkl+CLlyvAvAG8+f1Z/JJOZydsPv3OvtJd+gzY2SUCoU0U0gzPVG1bmabwfP3juFN12+HzxnGKz48pkXclOjpqX0Thh4zqwwMnOnVL5tPat+Qc3PAK14BvO99wMMPn/aXwuHchqtpDmc9bP20DuctI49oJlmeK7rYTJEKieV2iigVSIU29khMHe51crz74BI+/cjMmo/ZnA/wn79/FRb2V/G8738Ml73yWNfl00OdtdyLJyurbl806Sj5fFUuHzVdf13Lea4z9TlFJWBdmitLvp3B0RmBq4EOFxTsdoaU3RNTqTrRXFYzWo+0+649wh5caCERCocXOtmj2VqW5gAO3zWGT/76dWjOB5t6jsyYeJZ9jkrAsW+6Cp9T+EwPaKZqIbg5BwecgjOtN6UECDx99p2qheekrrSIQSam71ZKfbH4A0LIC8/Q83E4iyEMIVWqE0x8YL6Fv7/jIL715p2YrAVIhURmjI4289agBNgxWsJw2cNCK0E15IgyHQsjpYLHdHRA2ecoeQwlj+XicY9RSKULCqUECwualD7+OPDRjwLPf/6ZeT0czlm4muZw1kOYaaN1MW+nOm+vnQhkUqESMCw0kw4xzQRCj0HIjpvuJnuDyCIKkVK8+McewuS+1ZPRorHctTtG8ORsc8374pRAUtLlK1DyGYRU4FRrSHeMlDBbj1EJGC6ZqmKs3DFJKhtiei66Sp4DcDXQ4YKCkAqUaBO4NNORhlGqa6mNnbKa0d4NPzs5LTbJioMXAuRStZVjJXztby/B0LYWSsO6gfj8vWNdU8x+4JTkxkclM3wZLftYbKaYHgqw2ApRNs07OyH1GMFQyEEJybcFqwHPJ8LnKgaZmP7vAX/mcB6hN0g4E3oX3wrDbVf+S0/OIRESBxaaSIVEKjsrvIOeiSgBnrd3DLXQQy3wEHKGodDTttfG/Mjm2ZV9bYlNiO66W7G4LRiLi8ArXwk88ojOKb3tttP5qjicJ3A1zeGMQm2WEfZBR+ck0TCGRpk5TLUT/WexlSAVKr/cOp4/dmLtddt+eOoxii89OY/h7W3c/vZ7+5JSYLWRxgsvHl/zPhklCAtmdIzqiWrJ01svjBIE5gBWCzxcPFkFLxze7EHLEdMzAlcDHS4oZOZsav8stBI9MTUbKfY6gNaafu6xWZxYiXBsuZ3/3J4zjyy28diJRn7ftomWthm+/GeXgfkCL3jLY6BcoRowbB0uYby6/vR0tOKDMwJCCEKfouxzlH0tXysHHDtGSyj7DNyci20tLXk815kCervwXJc/rDkxJYS8AMAtACYJIT9VuGgIwIZuBISQEMDnAATmcf6vUupXT+3pOjxTSIyRBqC7RULpNV27yhsZ18dEKAgp8eUn5zE9FOosqHUE40XYLhMhwL6pGnxGwZg2xRgqeagGen2Bce3Oq9e9eJcVNqfaudEeXhYWgOVl4IMfBF71qtP9qjicyzjVmubgMCikQl/Xxc3dh84hTYXCYivRLoxCB8KnQqKZCMw1YmRCG8AJ1am98QYd8zgVePDoCq7bOYLPfaSK97xjDM/5zgh7XhDl4e/90KuRmhoK8eYbtqMVZ6ukG1p+QdEw01tucqcD3tFPMUZR8hlCn3atAQOdQ6Bb5T19cDXQ4UKBNOdWe47NhASnRHulSIVmnOUktbj6KpXCJx48DgD40pPzXfc530jw9cNHVj1W6DE0IoGv/vUlaMyGeMlPPIjSiJ6WXjpdW3V9SlZPZWshRyvJ8omp1ZsGnKJsMkrLHkci9NlcSIVywBGnAsy4mAMwjr/nds1cb5XXB1A11ym+sisAvmmA+44BvFwp1SCEeAC+QAj5V6XUV0762To8YyhOTFtJBo9RtFOBTGpi2k6F0ZkKCKnXzPbPtfDAkaWBJ6Xc5Jteu2MEZd+4iVGCsYqPgFNM1UI0kwyjZR+UEkzVAn2gKRQRZk5/UZsg4Nrg6KGHAN9f61EdLmCcak1zcBgIwhhRnMp1dAapbgBKCUSpzKelUSZRjzoxMIToKe1yOx3o+T14dAX751r4yvt24IsfGseua5ex/Yb5DW+31nmn3Cc72ga9l32GViLAmW4g+pyiGnj6a0YRcNp3zY07Ynom4Gqgw3kPKRWiTECqThTVSlufY4XJLD26FJnMUoAXmmqilzEWcHip3ffn1+wYxlceXUbS5Lj2G/djcl8dr792K2bqMbYNr47LopRA9gxwqgFHnElQ2lnL9Zhe29UxWpqcqkR/biilUPGZbvD11MhzvWauSUyVUp8F8FlCyF8ppQYLMOu+vQJgZ92e+XPq+00OZxRJJuFzmq89MErQTgRYSDBTj5EKla/yLrUSNGPdDRdSd4GW2tnAj+UxAqkUmHljaTMjfVAJTYcoFRLTQyEWm3pi0Bu2zinBygrwutcS3PQc4J3vdKTUoT9OtaY5OAwKOcAq78bkVWv6I9MQTIXCUjvFcjvFgqmHFqmQEBIQcjBtUdQi+OL/uRzHHxjFLW9cwHXftB+zzY1vu9FxRxNk/TU3UouRsgcFZYw9tMtk1eiiKCWoBV5ft8pz/XB1NsLVQIcLAVEmEKU6HcLq4o8ttzFS8SAVkAmFNrKchBbJ6HrEdC2Mln285oZJvOw9C/i4mbZ6jGL7SKnv9T1GzAoxMFTiaEQZtgyHWGmnuUGcvY8txnAu4LrRF2f6MmrkbHoTpbtWXgjmRy1CyO8BuApATv2VUi/f6IaEEAbgLgCXAPgTpdQdJ/tEHc4cbHC7kLqT5EOvCbRTgWrA0UoEykZQnYqOxvQLj8/hiZmO+cWgbwabx+QxiiTTj+0xHQPDjCmG/Xus4uf3TfrUi5Vlgje8Drj7boKf/InT8Wo4XAA46Zrm4DAIBpGYCqnyRqBFJmSus5RKNwpbicjrcTsRaCY60uDESlS4r0GeU+dJHX+ijJlHhnHjtz+JbS+ewToeRl2wByC+Rq3nlOTZf3pCqtfS/FqIlSgFJbrW+4zmetVa2P8YstZjOJwWuBrocF4iySTaieiSM0ipMN9MzPZJxwfFctAiF900MW2U8Ue/NI7v/dlF1EaAq7bVkGwgZ2OUouQTNGOBiyereOxEHR7T515CSKfOMr0pCHQ099wISKm5rl357br/c3yVdxCJ7N8DeATARQD+B4D9AL46yJ0rpYRS6noAOwA8lxByde91CCFvIYR8jRDytdnZ2UGft8NphBWAW0ddQIvAZ8zBJ86kdjFT+jr/+egMnpxprDLY+Oyjq///7Rkvr/qZjX+hRB9cmHlz1UJPry6Yg5nPKEaMSyOjZBXxnZsDXv1KgnvvBf7pn4BvcotIDoPhpGuag8MgKOot14oVEEqhHqVdByFL6pTSGqjEaEozqRAlAomQeQTMehl6RaRC4tHjK/q2K5oE7rqmgdt/7R7sffHa0TFr4abdo3jZ5VN9L7Mk2263EKJX0coBB6edtV0b/wV0Yg56wTdwsXQ4Jbga6HDOwUrMes3lsh7pWduYc1pyKpTOep5vJKhHuq4Wt1rkSU5Mr5gcwZ3vuhz331FCs67r1WVbhnDN9uF1b8fNui6jOme05DH4ucln55yrf9bJLC3+DXTOzr1E9FzfNhmk8o8rpd4DIFVKfVYp9X0ANhXAoZRaAvAZALf3uexdSqmblFI3TU5ObuZuHU4C/dwi40zg+EqETKrcqCIzTpCA7kA1kwz3HFjER75+DF95ah5feWphlXi739vZvl+magGed9EYOCUgACqBh+mhUHfQzYR0rOJrxzHeIaYWrEfQLSVw++3AI48Q/Mu/AG9848m/Jg4XHE65pjk4rIfioWctIyIhdTZpO+0QzMRoSuPMOvEKRCYmJpMdV/RB8ORsA0utBA8eXcZDx+r40z8T+NdfvhEzjw0BAMqjyaZ+p+fsHgUA7Bwrd8XGWNgtGAC5T4A176gYD4HQZ3nzsTN9dQT0WYCrgQ7nHKy5W9ozkYwyCSEVpFRoJnqNNxF6cpoJabYBFRaaCaJMb54Up5qtJMNTcw20zXbKIFAK+Nc/3YFDT/h469vnsGXnZmRsFCVPO+zWQu2865nkiWI97CdxKBJXOy3tvdq57mQ+yCqvdVM4Rgh5HYCj0BPQdUEImYQuekuEkBKAVwB4x0k/U4fTgsxkHxURZxLzjQRln+X5dzaiYKYeIRECzZjizv0LaMQCKaebEAuTPPx320gJ9x9ZQiqAN9+wDVO1EP/+4HG86OIJ+B7FcMmDlConpMUJKaUAUd3fv/3tQLkMvNwtHzlsDidV0xwcBkWxAx9nEpU+SQHCRL+kmdTe9QCaSZYLOZtxhtl6jFYikMruAPhBcN/hZQDA9qEK7vnAHjz5ma2YunwJI9ub0B44m8NGXXiPd4jpZC3AcMnDiZUIPtMT07LPwMwqb/G+ej+PHJ4RuBrocM4hlRIlaO+RogQiSjWhDD2KdpLlxHUlSnGiHuf5yCtRimHi5VmmgB7CfOx+rQv9Opaxe2z1ll8/PPpv2/DAp2v4lrcu4cYXRRvfwICbZp3HNCmt+Bwlj3U0oxvUQ9pLTOXqVd5zXQYxCDH9DULIMICfhs65GgLwkwPcbiuAvzY6UwrgH5RSHz3pZ+pwWiCkgtdjCm8nopnUq2X6ZzrI/dBCC5lQaCZZ3qmPBgzvJdATWmY66RUT/0IIMF4NEHoMt1wygS0jYT591XrTPkYYhEACOHAA+OpX9dru619/ki+Cw4WOk61pDg4Dodh0X4tMCtPdHy4YFq1EqTEJIohSiaV2ijiT+aqa3WLZCHYzJlr28H/fuRdHHqli321Hcc2bD4AydE1pB8VG2aw+Y/kq2nDJw1DJw2wjBqUEJY/lhNTj3RPSflMBhzMOVwMdzjlIMxVNe+QR840EE1UfGSVoJ3pNlzOCepThxEqEqq+pTiPK8sGHvY/e6euBhdaGzyOLKZ76/DRufkUdb/zulU39DiWzPcIpwdRQCEoJyj7PvVY2s4ZrJ6a9q7znvflRgUwuA3jZoHeslLoPwA0n+bwcThHW0KgXvWsKmdCGRu1EZ+O1EmGy82S+asYIQbudbtpSmTMCpfRaQdlnmKgGIISAQsGjNHdo9Bjtcnvspy1ilOCxR4DX3A40m8ArXwkMr7/G7+DQFydb0xwc1kO3cVGnWvYSUykVCNHXacUZMqHw2Ik6Lp6sYrmVIuAMBFpiYa9nV9iaAxJTW+YP3z2OmadLeN73PYadN3fiYOYaa6/xDpU4VtoZqgFDo6BjHSp5ALqdd4vwjX60qDOlRoJh13oZJfAKudMOzw5cDXQ412BNi6RSSHqI6fGVCCNlD5lUSKVEnErUQo56lIJTgnsOLQLQ2ysrUYr5Rozxql5TObgwoPNbAZftKGHvHzyNq/dU1ozQKsJjBAp6Fbns6yYdJR25mq2ZRe39oOB9/FfOdWxITAkhlwL4PwCmlVJXE0KuBfBGpdRvnPFn53DSSIRESFfnZYueA1OcdnJJ20bv1DImG3rVTOKeg4tYbqcDRSAUQQnBzrESto+WcNW2IWwZDvGiSybwxEwDHteOYiFn8CjdcH3h7rsJbr9dH4o++UlHSh1OHq6mOZwJpEKBm5JbrJW9HXkbxZUKiZUoQzPOsNBMsHU4w2IrRTXkqEcZhNQ1mlGam9Kt576bCqn1RiCYOaoPOhffehzPe2mEebo08O/xkn2T4FRPGz71yAx2jpYwWQswFGpiyijJiXIRdj1taigANeSTWzJqjD2sK6/jpc8uXA10ONew0EoglYJQalVNlUrr7z1G0TTNvtCjiFKJxVbSRfbufHoBj51o4GWXTYJSgoeO1Xsfak3EdY74vr246oebYAMsvnNKkEmVJ02stDOUfZ4TUmv8ZrcEPUY3TTLPN1IKDGZ+9OcAfgFGk2Amod96Jp+Uw6lDFCyxu34uuonpSqRXxVLj/mj/tuQ0SgQePLqMx080BhaF27cJIcDlW2u4ac8ohkoeaiHHDbtG8Morp/OVBc70n4Cv/U/x058Gbr0VqFSAL3wBuMHN4R1ODa6mOWyIjVZXe5HJzsptMU60d+1MKq0tzYRCI85Qj7WLZD3SBLWd6IxoQBPR3tv3g1QKH73vGL7wyAJ+++er+JX/tg2tRR+EAKNbBjflAJAbEw2VPLz5hu24ac8Ydo9XCpf3PwhZA48tQ2EeBWOzSu1mDCUkJ6gOzypOqQYSQhgh5B5CiJNnOTwjaCcCUuramhqTOAu73tuMs9zN3JrORaneCLRYaOra+pWn5vGph/u7kt962WojVpkRfPldl+HT/zCKowe8gZ6zjczyGEXFrBOHHoXHdR20ETBeYXJa6tXaXYAYRGNaVkrd2aMD2dwnncMzDgXbQe/+Ry563CIPL7bhc2oOS3pKuthM0E4yxJnEfYeWdFAx9JsnFQNokwjACPAdz92F8UoAqfSb0RpfUErgM/2YPCenaxPTL38Z2L0b+Ld/A7ZvP8kXxMGhA1fTHDZEKhR8PjiBklJrN2umtnXup5tYZlLlzcAkk2hEmc4nNWu6B+ZbXU6+g/BjKRXqMyH+4937sHSoimtefxilYX0AO93eQto1UoKSbi2tJZ/j1QBRqteQi/EHHrU1f3M6KoczglOtgT8O4GFobaqDwxmDlaVlUoFRhZbxQ4kzgUacYazi68xnITFbj9GMBTxOu4lroU7Zf/PtdO2G32i52xxOKeCe91+EuSeG8B0/fxQ7Lx7srRJ6DFIhNwAFdP30GIWUKiehlph6jKLsO2I6yMR0jhByMUwaCCHkmwAcO6PPyuGUYXWivShOPTOpO/ba0UxPUJUCltupfoNHGQ4utPI3dSsZzDCDABgu+Qg8hkrAQQm0I6NZ67KxAtaFzFsjLmBuTv/9i78I3HGHI6UOpw2upjlsiH4bIv2mqPZ6mex05i0xVUrlMQaA1qEKoWuz1UlFqcgnp8Da8TLr4cv/UcanfvsatBYC3PK2h3HVGw+DmLK6mTzQWrhxr9pOTLnJI7UIOEXoMQyFXJvcwUxMzUHQTgkcKT0rcNI1kBCyA8DrALz7zD09BweNVJq4FyMva8adzb7ZepxvB6aZlqTFmUSaaZLaD4NWn1dcMYXd42W87LJJPPbJbXj6i9N42X+dw2u+IV3zNr2SNG36RsCo1t9TohMlfLMx2CGmJP87dBPTgSamPwLgXQAuJ4QcAfA0gO84o8/K4ZShAKg+5xuRa5X0YamVZIgznZO32EzQiDJEqcSHv34MBINb+dvuOSV6hdcS0VrI0U5FwXXM6owIhCLgavVOvVLA7/wO8Pu/D9x5J3DxxXqN18HhNMHVNIcNYaMJihBSrTp8RKlAJeAQSuXNO7vKa7X6QilQEDRjAaH0xNRmRmvXXYVjS4NHDvTi618qYWhbG8///sdQHku6pqxyQAkGALzokokNr8No0aiDIm3r38M3zuvEaEtJQWNqL8+IOuejDM4TnEoN/F8Afg5A7cw8NQeHDjKhEKU6AoYQvWWy1EpQ8hgOL7YwWQuglEKUiVx7moi1857XUhHsnazgqdmOEVIt9HDjrlEsz1M89PGtuPQFi/i+H2+t6yJeDTiasX6udko67YdoxplJpuD5xFSv8ppayjsTU4cNiKmJenmrUuoVhJAKAKqUGlwp7PCMQykFQrQbrurjo2uJaSPOIKSOhLGE8gNfPYRESLzlJXv1fQFdIcRrgVHdGZdC5U680sTEjFV8tBKB0NMZdswYHelIBKzaMxMC+ImfAP74j4Fv/3Zg585TfUUcHDpwNc1hUPQz+BFKrfrQ1I03puupydOzE9NUKEil76uVpGgkGTxGsNRKchJrJ6Qr0dqd+H44dpBDSWDbngzf+dPz+NRjx0GZftwiF80GJKaEYKBuvSXmnFIEnKJF9e/BKEHZ068OY3piWowy8DkFMnlemnWcSziVGkgIeT2AGaXUXYSQW9e53lsAvAUAdu3adepP2uGCRSYUGkmGTErIVNfW+WaCcsAx14gxU4+QFJIlgPXlD2sRy/GK30VMLYbHJX7t3SewZWe6qnYxqu/PflZUAo5USKRCwGM0j8mKUoHAo6gGPNfil30vfy4+c8S0iDVfBUIIV0oJAM8BAKVU0x3gzn7YQ45SCv3OI5aYLrdSZLJzHakACU1GN5NxZw8zW4dDAPqwYiMOmMlnKnksdyXjxq2RwExOC2/0ZhP4xm/UpPSnfxr4278F/M3nwDs49IWraQ6bQSbXl0JYRKlAJhWOLLbRTgWaSQapTN5epiemS+0ET842UI9S1KMM880OMU2yjQ9TvfjSv5fxy/9tC97zjjEAAA9kTkp770v03PGO0VL39yMlXL9zBLddPjXQY1vphTbx0EYe5YCBU4pyoImtnphqYmqVGnpK4A5ezyZOQw18IYA3EkL2A3g/gJcTQv6u90pKqXcppW5SSt00ObnaSMbBYVCkUm+XpELlBLQZZ1hsJRASWGqlSLKOeedG2KgttmVIx8gcP8jxmQ/rVb09+zKE4epblnze1cwLecfkyDPShtCjGCn7qPgclYDn0VlFw09LSJ3MQWO9iemdAG4EcA8h5MMA/hFA3k5QSn3wDD83h5NAlOrppJ1aAp0paiYkMqlttZfbaZ6ddHC+iblGovVTavAQd0BPWhkheP212/Cnn3kSPiMQkmDrUCl/k1nXXaWAodDDYisBoTDRBh38zu8AH/0o8Cd/ArztbaftJXFwsHA1zWFg9J2Y9hBTaeqpMH97nKIVC20ml+mDklQKhxfbeHKmgemhEMKaH52ElrTdJPjb/zmKz360ikuvjfDDvzqHRpzlLr790Jt92hvGvnuijKlauO7jMtqJqrF13TYcGxFDJWDgjHQdsAixngKdXNNNGh07nH6cUg1USv0CtJsvzMT0Z5RS33mmnqyDg5AK9SiFkPocG2cSUunhCgAzodRO570xMr04stjGseX+kgmlgDdcuxWUEqwsUvzuT02i1aC46aVtVIf71+pqwPRjMz015cw05+p6CsopQcB1064ScFQDTWR93i1hc4S0G4NoTMcAzAN4OfRAjZi/3SHuLENWsMhW0MTUCsMZRe4EeXSpjflmnB9G7jq4BEAfPhSABw4vb/hY9h8BIwR7Jir5gYQzitdePoWbdo/ma8B2p97CElLbeVdKT15/6ZeA227T0TAODmcQrqZdwLCNuo1giWkmZD7p09nOIu+S65qqa2ws9JpqlGp5RJ4HrRSenm1CKh0ETwmM1GJzmDnC8Ns/NoXZYxzf8N+W8Y0/sIyDiw184aH16/WJlW4TEEL0AUwqaI2WCZpfD5QQCPOMfU4wUvYQeAzVgGOhmZjDV+GgZV7f4sSUmKgYh7MCrgY6nNWwdToVEk2zXeJRkp9x7caJrW/tAcw5Dy+11r2cM4okIvifPzeJhVmGX/rjmTVJKaB1qLbBON9I4DECTjWtCjy9IWLdeANOUQv1xDTkzuBoPaxHTKcIIT8F4AF0CpeF63s+w+h3mCoekACtJdLOusr80d0kIRUSoTvny+0Ui80Ey+0U45UAR5ba+e3t5tpSe32tkzU4EhIo+Qwvu2wqN0m6dLqG6aEQldBDyUwXigZKdoWBEgLGgc99FvjNXwM+8hFgZMSRUoczClfTLiCsRUAHjYGxq7yNOMOIiQ8QUmGxlWDrsF6Hjcz6mN1ECRjNDY/ahphKqXKdp1LAALL9vhibEth1SYof/pUFXHa9PowN6pRehELHqXfvZHWg2zBKkAqFiaoPj1HsGiuDEoKyr7v/VqphYe+fFzSmgNNQnQU4bTVQKfUZAJ85bc/MwaGATCp4ZhIZpQIEQEYJ4qy75lkpxCY83rpw/c4R3HtoCSNlD1IC/9+vj+HxB3z82G/OYd81a2+iBJxipORpOVvE0EoycErzTcSSp5t1AWP51mDgaV2+09mvj/WIKQNQRf+VbHeIe4bR6waplEIzzhBwmh++rPlGKlQ+MU2F1IHEUuaHqKaJiEmExJ1PL3Tu0/zd22HvBSXAi/dNQing4ukKRsoe4kziDdduxWuu2YoHji5rx0bemaJaWN0powT/8AGCH/x+gr17gZUVTUwdHM4gXE27gNDPQRfQhNMfICnNru3WowIxNXnPgCalUSYQZyJf5c08/bfOkCaYM3EGJ4uZIwzv/9MRfP/PLyDjCd76Gye6mpE+H5zo3bhrBHcfXBr4X3oxp9SSzslaAJ9T7BmvYLGVQCog4FpH1U8/aiPCHM4auBrocE5ASAVOFTKpNwEJ9EAkOQkJxHq4aKKCiya0lvSBrwa441MVvOVn63juy9vr3m6o5GG45CHgDEJGuHLrMCgBRsseAJ1MwRlB4JmoGCNrcDr7jbEeMT2mlPq1Z+yZOKwJKVcbGekpqMy7SoDp3mc6AkYqhUzoP/owJdBOBY4vR3otS2JV52kQvOKKKUzWAoxVfBxdijAc+pioBpitx6iGHJVAu+96lKLkd4wwiqCE4NffzvBbvwW85CUKH/oQMDp6Ui+Ng8Nm4GraBYS1zN/66ZCSTK4ieXZFNyqYwVkCCuhJapopLLXS/Oex+bPUSqCgt08GMeTohVLA5z5Wwd/84SgoBb78tQz1kRn4jOCVV27Jn+tmomBsFe7n1t4PnHVC6m0Wqc8pGNFZe6NlH4utBNWAY6zi942B8RgdaG3a4RmDq4EOZzW09Izkg5Y4k4hTYdIm1Ck1+sgG1kdX3xzj1//yOF7+Yo7DC2RdR/OKz1AJOMo+w3yTYPd4BYcWWhguWWLKMVr2c1IKDB6/eKFjPeruXsGzBHb6WYRUyGMILKJMIpUSrUR38JtJZoThelr65GwT//C1w1g2q7oH5lZbYxfxqiun86/tP4bhspc77TKqDyoln4EQYKIa5IJv60gGrF7feseve/it3wJ+8AeBf/93R0odnjGcVzVtM6TkQkRvzQT0tDTrIYprHXY0iZVdRFYbF2miutJO0UoztFOBTEokps7WoxQrkZZMNOMMzXhzDcDFWYY/+JlJvOs3xrHnsgS//XfHUB85DkDHd33s/mO468ACvn54CQ8fH8xQ9Q3Xbs0D/NYzIGK0O+y983Md7zVeCfKOv5Vk1EKOodDrOxl109KzDu5/iMNZh2Lzzn4tlMJyO8VcI9GOvEbPP8jH3lwjzk3p7P1FqeiSrgG63gHAZz5SwcN3a6393isSDJe8fNuml0zakhb6LDd5s1KGasjBTZ5z2WcIPda1tmuN4BzWx3oT09uesWfhsC6sXrQIG0egdVCaAEYmQ6+dCviMohFlqIUe2kmGRixw1369trvQTLB1OMQ/33t03ccNPYbn7B7F3QcWQYxhx2jZBwHyoOCAU1R8BkoIJmsBKCFmZWFtx7G3vk3hiks1MXXddIdnEOdFTcs7ykqBqPPzPSSlOmUdTirkqlxOKVfneoqeOprfXspco2/vT0o9Xc2ExGIrzfOgM6FrdDsR4JSgnUhEaXxSLrR/+79G8ODXAjz/2w/i1d/cwOePrTY3Oriw/ppZEZdMVcEZzVfMto2U1ryudo9kiDNpDlGaVFOiJwLVkOcHPUJ0JEwl4OaA1u/+zr9/m+c4zosa6HB+IckkPEZzWYQy59ulVop2MnhKhFJaz//5x+cwWvawaJx733zDdvzrA8e7rnvFlhp2jZVx56dLePdvjeGml7ZxxY1axlb2OTxGkQqJiWqAmXrU5UouhYLPaJ4/av+209KRso8RU2+LcOZvg2FN+q6UWljrModnFlLp7lGxq2SnAXZiGqXCrPLqnKdMKtTjDFIqzDUSzNZjpIUD2SDnJUYJdo2VcdsVUzm53DlayrvknBHzR2eXWlE3oyR/o1p87nPA93wPIASwayfBW96CvgcZB4czhfOlptn1StFnxX8tnGvT1d7szc1CGrLYi0zKVRolqTrGb13PwdxHIjQ5a8Uiv/1sI0YjzpBkMteU6vvXdRfYXC7p4hzFwowmxt/x40v4rb89jh0vPoIHj2/skL4Whksc12wfxpVbhwBoB8lvuH7busSUUk1Ch8zEgBITCUYJJqo+yiYw3oKAaH0pJX3X5M7Hpsm5jPOlBjqcX7A1OUqlqblaprbcTtFOB9s4ObrUxofuPYqP3ncMAHJS2g+EAJdvHcLTD1TwJ786gUuuTvDWt88D0FNURnT0VegxjFZ8lH0tVwBQqIskb55aaYWtjVO1AGV/9dzPmb8NBvcqnYXoPUQqKEil0Iq7tU6Z+SOlQivRxDQWOmh4oRljvhEjkwqLzSRfbbCoR+t3oV5eCFvfMVrGrrEyAN0dp1Q7jnmsEwPDC3l1xZ16APjzP9cxMHfcAczOujeng8OpoONCqFdQNyKdSqm+a63PBlmVphu+EU5FRwRoYttPH1ScmNrfXyprsCF01rMhmanQ0ogVUysbRrvfTgXmGwliYyYnpOpa1+1HiNeCUsAX/rWM//fbt+Ivf1drGsanBLbsXN8ZfRB4jOKSqWrXxgpdhygSc9hiVHsE+Eyvp3mMglEt46C0O/KFEm1+xBmFG446ODicDBJTc+NMmE0V/bnWTrRvyiC44+nBey4lj2H/ox7+8GcnsWVnip/5/VkEoX4cSghKPoPPrZbew1DJw1BJE01GKcrmHGzRe6adGCCCy2FtDJJj6vAMQyoFarrP+iAHKAm00gzD8Mx1bFwMRSqt067u3reIwPvvPIiFZoJrd4zkmtNGgYx++pEZAMC2kRBHl1YHDpc8lq/vhh7FK6+cxlVLbficgkBPST1G8yzSYl5dnvEXAz/+48Cf/Rlw++3A+95nnXfdCcbB4WQRCwHAg5AKlChkEvD7sAK78pvJjhTA/gzQGZzWoKwXg2Z9bhaZefyNNpo2Q0z7PVc9Te6jG1UqJ57291dK6/NlO0M14BBKocqo7txnEo1Ib5604gxCKTTiDOUoQ2w0TFIBT8wOpvUsYn6G4a9+bxR3f76MfdfE+LYfXer8Tpu+tw5Cj2LnaBkXDxgFU7wdMVMAjxEoaFJKDTkdKZmJQeHfGiEd2YZzm3RwcDgZxKmEUgpxqld6E6Lzn+NMrGtANCjmG91JE8MlD5/7UBWVIYn/93/NYmRUf44CQGDIaNnnkFJhuOShFvL81MopASUEQSGLtJeYTtYcMT0VOGJ6FkIolf+PyQpd/eJhrWEiXzLBkQmFepTlcTBWkyqVnozaWJiZun5zFjtL45WgLzG1eaNRqnVanBIMl30woytiVAu+PW4F4p0cu5Ihpt/2bcA//zPw8z8P/MZvAMxlCjs4nDRSIcEIyeuAlICiQKb6x5/oyBKWX1+ZySCjDFIqpFKihP5vyrWiVk4VUilQBWzUnOo/7eyvOy06k1vYjZLV15V5QPtcI8bOsTKkUphZiTBe0eZtx1ciXLalhkxKnFiJ0E4EltopTqzEIARoxgILNNGZ0eZ+28nmnHfv+6qHP/jZSRBJ8e0/uojXfGsdtPC/4lQ2mSkhuHr78ADX63YuLnlc//sgpvHIKZZaqc7i42zVuhqgV95cJp+Dg8OpoJnoRp+u2dKY1KkN13jnGzE+9/gcqsH6h8vPPT7X9f1Nu0fx3J9YxBu+awWjkwKBx5CZrZfpmjZ4qwYcJZ+hGmhzN5trzQwxDb1OHdxMbJfDxnCv5lmIot5JmjU8oVT+82ac4cmZht7HNzoo+8aOU73KK5S+8v65Jj549xEcW15NPgHtONZlVGS+LHk0J5ihp3ft9RSV5OtetZDnZJSzTpC6Jas/+7PABz8I/PZvO1Lq4HCqSDLdRc6nn6Yu9Is/ATrkTpiJqZ3wAXp1aj3y00zEaVn17Z18DqqL7ffYa3XOixEuxeuKPq+LlB09vpUz2LXfRAjEQmCppUPVU6Ews6LlEEcW2zi82MofZ7mdIhX9daxSKTxwZLlv3l5mllam9kTYdu0i3vSb9+N139Ehpfvnmji+Eg207gwAz987ln+912TxDYpeU7rQ01Ew1iNgvOLDY3paWi5M1ou3c2YeDg4Opwoh9SZKURrRTDLE6foNP0s4GwM6n8t6CXf86VVYmvVAKTA6qW/nM23kCQDjVb0ZMhR6xp2XwmMEYybL2jMa05C7Q+2ZgpuYnoUorqCJwsTUdmziTHfyJ2oBltspaiHPTY+OLrUxUQu6TJHWAyEEr792Kz5071EQAC+7fAqtRGDrSAmE6MlswJkRgtNc9M0pwXjFz3WlIyUfjTjDO94BHJ3x8M4/AF7wgjPw4jg4XKBIhV5zsvUhySQ4I2vmZGYFQx6PAVEi8+lWnMmufVEtC+h80C61EpR9lksK1sN6a792QmuvJ5QC24B0pUKuMj8q1rHeyamQCqnqziHtOO2ufj7tVJgGnjYvUlLrQuNMIkokmkkGZczmLBd+YrYOqYDjK/0bfEUcW47w+EwDUSpw0x5NHOOI4IPvHsb9d4b4tfccR6kq8dzvfcL8L+jkZd1zaAkA8Pprt274OAAwHHp45ZXTEFKhGnAcXWrjmnWmpcUpqf63o3LJBqcUoadX1WohNxEIFJyRLmJanJg6Yurg4HCqUErX7HqUgVMCqXSz8XTaIETLHr7yR1eiucxRX2piYkvnMyXwKBT0Z8+wkSxsGQ5z7as2/NQyOkYplFIIPDfXO1NwxPQsRPFQZjVZSnUC321IfJwaB16hu03/dPdhpELhDdduRSvRb7oPbRAJQ4mx/Yd2ZNw9XkYqFAJOMT0UAgqoBhyE6BVdSgimh3QsTNnvTEyTNsP3fRfBRz8CfOM3UUgJuMgmB4fTh0RIhGZimpnMzNCja+ox7SRVSgVBNBm15C3ORJdz9kqUdhFT27n2BmgK91ulzS8TCoH5lLFaebVBXcjE6lzR+WaSW/GnUiIo7L0Ko02qhZ3r90ofLHk+sRLh6FIbl22poRkLrERpfihKMokW1bmjM/W463nYVd1BBpl22mmve98dIf7iHWOYPcrx0jc0kCYEpPAaHJhvYvd497SzEQ8WkaAzpDuv/WuuWZ/Q2qgD/TUFIOEzijiTWvtLGTyTTQ1o4ukz2qVF7pqYuiLv4OAwAJKsu3lYbDAq6Po7W49Q9nVTrJ2sPVRpxNmq5If1EDc4PvdHVyJe5Pj5d87g4ivSnPRSAvhmpS/OaP4543OaP1+bU8qobtyVfO4mpmcQjpiehVDFVV7z7slMsHsxV09IBQXd2f/sY7P5bVqpGLjTZF0aKdXuikMlT2fxMYrhsgdGK7lbY2gMkUbKPrgxyOCU4K67gP/6X4H9+wn+5/8EfuCHJag7sDg4nDZIQ5yskVEmFaJMIBWdXMneqaedGGZST8VEcesilV2koncdOO1DDtfCegS2uH6r0JEmrIekUOMs4lRAGPt9m3NHCNHmcFIT7SKkVF0NvlQo+JyYdWb9+0epQJxJHF9uI0oFOCNoxgLtRGC2HiNKxSlFWkVNhv/1CxP46n+WsXV3il/+0xN5Tl5aGLzefXBpFTH90hPdmqheXLVtCGMVf9MGVXZKCugDFiF6WiCV1hR7RlsVcoZY6ExWj9Muo48ieteBHRwcHPohFd3EdKGV5O61Uum6v9xOwaiuR60eYtqIM5Q8BkYJPvnQia4tjvWQtBg+/0dXoD0X4mf/cBaXXpuAM5pLLQKP5sRTyv56Ud+4ju8cLYNRgpGS57T1ZxCOmJ5FsKt6Iu+468NVlinE5hBVXHPLpMy7/cvtND/wferhmYEf0xLTb7lpByarIaZqIY4tt8EpQdljqPocK1GK4ZKHepSBEIJayExsAEWjTnDbbUCtBnzsEwKvfgVHnLk3rIPD6YTVks83YrM9IRGlEoutJF+2jVNp8oT1B7Zd55dKIRWdySmA3CTNQohOjAshpO86re1wZ0J2ObAWiWaRMFJKVk0tByGmaR9iqnXznYacVFoPb++vV8+p3Xc7K8+p0H/s9dqpgFS6rjZigTkTrSWN3rQeZbq5dxKrZPbX80sCS3MM3/xDS3jdd6zA89e/XfF3TtaJnNk5WsKl07XNPzF0pqT6a2K0VVqqwSlByWO4ZKqKKBVIhG5e6GgwV9MdHBxOHrYGE6CLGKpCDRcSnb9FQb6hFD750AlsHQ7x/L3jALCKuK4FJQlGqgw/+JNzuOom3RjklCCBnpaWPAbOtHxhotq/SPucgjOKfdM1LDQTjFec6+6ZhCOmZxEyoScP9oBipxapkGglAqkJcxdCGUdIiZGyj8hMCwadkr543wS+fmgJK1EGe76cqIbYOlKCzyg4pagEPD98hsb0qORrd97QY2g2Ae4TjI0SvP/9wM03A+WaMT9y01IHh9MKG38ipSYUmdCkilOSTwszKZFkBWJa2LagpHsCKpXmXJ1IGT2NJQCE0jlyveZBdoU2yiSqpjYoszJryapd602EREhZl85TQRPGjcpUJrrJayb087H3JZU+qDCQ/D7jXmJaiItppwJKKRxaaCMyZhrtRG+VHF+OdByMApbbSV5D53riBQbF3V8I8Y/vmcD1P7ACOgr89z87sUrSYOUYRXz8/mOrfoe1cDJTXG5ig0oey2PDOCUIPO0dQKCdd0fKeo0t4BQR086TlYC77GkHB4dTQiIkAmG3d0ReAyMTFWM3fxIhoLLubRu7OXhiAI2/RdpmoFwiqGb4yT86golah3RabfxoxUfF57nZ21qwU9TJWgCfd9Z9Hc4M3KfNWQQrtM4PVCZ/NMl0TqmemGoL7X9/6AQ+fO8xrLRTPD3bhCy4da6HasAwUQ3yN70VdFcCBp/p+BePEYyWO28861bGTZ7dl74EXHkl8I/v1y6Ot98OjI93DkxuxcHB4fQikzInZNJ8iGuypjrusj2Ot9b8SBj3WFHQXdpIqXqUAkDuMCuUQpR2NjPsfdj7B7qNiGwsla1dRdffXgKmVEdnuh5SIbsOJZlpzlmOKwukM8lknjcquw4y+nbN2MZoKSy1k5zc2olpI87QjO3r15l21qPBNJ4Wxw9y/N5PTeIPfmYKUYMhWvZz3X5sYrwsPvHAMXzm0dmu2w9CSm/cNbKp51QEN06S44WJgF3bLfss11ONmcMZIQQjZR/bR0vamdLVdAcHh1NAKnREWSokWnHnbLvQ0g3BnJiamm6bi48er+fTVamA+48sb/hYSYvhzj+9Gne851IoBVTD7rVfbvSiE1UfQyWO0KMYrfgI1tCkFBtzgYuGOeNwE9OzAHbakPYQ0+V2CkL0Ia+V6FXeJJOYa8QQZrrxt18+gFddOd03tqAf7BS0GnBEqcBYxUeUCpR9Dkr1G7CYSQog1zFREPzh71P88i8Du3cDV1xBuqYBTm/k4HBmIGVnOkmJnkhqgx+90itNbicrECBNNmVn0igVFNH1Rk9MFRJzHR1kLsFAEKfaUE0akzUrL7TaxC5iCphgdIWyrw8XoceQZBIh745TUbCrstLcn+w7iYszucqZPM06v0crFSj5rKC7lfnrQUHM76dfq2PLUe46WzQzaiWZPhihmwRv+v+LAN7/pyP4t3+ogXsK3/Fji7jyFSdwz5E2gBIA4OMPHEfoUbzmam1MtN6a7nqwr9Vaes9e2N/b3pZRglrQ+cgPPT0hFVJPnANOV/3/qPiauG5Wy+rg4OBQRJpJRMa/JDLSjCgTWGwmXY07/aUuXMeXIzx0bAWLJsILAJ6Yaaz7OCT28Pk/uhwrR0r4b7+yiJdev23V2ZRRvRk4XPJzHb3HKAK+cW3ejOmSw8nBEdNnAb0arWYsMFym+SFOGKMjGwXz0a8fxWwjxgsvmTR5pfpgaN9CJ+qDr51ZTenNe8YQZxmGSx4CTlExDrseo7kJRhGHDgHf8V0ePv9Z4Fu+BXjXu4DhnlQC5g4vDg5nBHZiKqQCpypfd83MimsqpYlI6azVCqmQmMkqo3rllQK5lhLKGhJpMphrMkVnYqqdgCkIIfnKbpJpIhtwZvRByDvacSYRCplnpvZOTKXS2qBRQyqV6jabiFKR31dmJqcrUdpliNSKM5Q9hgP1FkbKHjKhiatUOuP5xEoEBU1EZ8zqV9tMTe3vuNxO0U4kTraXppQmfpQBxw5w3PKqJp73LUcxNim7AnasbjfaII9vEGwdDnH9zhHsGisPdH3rtgvo7L1K4IFRgomqj/lmgtBkVRMCIMr6TgKsjMPBwcHhZFCPUtRCD6mQaKcAl50NlygVaMYZ0oLHQRG2abhUIKbrIWly3POuq1E/GuAnfmcWN75IAH0iz6yMwW6DpKbpOcg01G0Ennk46v8sIBHdh5RGotfG8omp1AeZ5XYKoRQeOV7H03MtCCmx1EpXTUetW+d6sA5mdoUh9Ci2j5Zx3c4RlHxmMkq1GUbvxBQA7rwTuPtrBO9+N/D+968mpYB7wzo4nCnkGlNDBDOpco2oMMZIVo+eSZWvu+ZrubJzXWsuVFwNjlN9u8QQysRMVTUJ7RDFhVaSr2ABHadd+32UCr06m5lV4J66JKWWIqRS329njVZfrxFnOQm1BLkRZV0uwc1E4MBCC8dXIsSpvh87ZT240EKU6ttEmUAjzrDQStBMRBe5tfEvmx2WKgV87XMl/MJ3bsGJw7qv+xO/M4cf+pUFPN2ax10HFruiXg4ttvOvMyExv0nt6t4J7YpOTKzXRROVgTdTioTfY/YQRjFeDRBwiolqoONgOM3XeXvhtmAcHBxOBtJ8Dq1E9nyrNzNacWY2dPTnjv2c6XWGBzqNvfaAjb2vvHsfjj0d4CffMYubX7K2HnWk7KEacAyVOIZLXu5m75pwZwfcxPRZQJJJlP3u7wFNTLUph56Y1qMUUzLQa3dmFW0lWk1MHzq2suFjMkqMG5o+iJQ8BkAHp/uM5lmmnpmWckqwuAh8+cvAa18L/Jf/AjzvBQo7trk3roPDM41MdjKMFZTRQyqjv9R/C6UAoa/LqSacbaNvpERb4SuiSax19qUmIzkRuu4kokN0pVRIoclhwLVbuDCPFWcSNdgpqModFKNUIuCdiaklrT6nucZ0oZlg20gpd8ktB8y4ixM0ogytOAOjpGsKW9SdtuIMy+0UHqOIMoETKxGW2gkSM6klAJ6cbUAq3YBrRBkoIdpReEAmWo9SHF5s4/Ittfyw8vQjHv7+naN4+J4Q23anaCxTTO8AWM+nqO3uSwXcdWARgK6/X3xiDgutdMPHLq7gjld9XDpd6zKRGvT2RaLJGc3jvraNaOf1ashNbBBBTOTAK8IODg4OGyHKBCghSDOJVpKhnWZoN3W955TkkpSi8VEvNts4vObNB3HFyASuv0XXfLGG1V7Z50iyTs1zmaRnFxwxfQagdWGdbsyqaAOz0pBJhdl6DKEUFptpPjGwhxQhFVpJhq/tX9z0c7Aui0opBJxhy3AJS60EjBIMl738+ekDDMV//ifB93wPsLgIHDigzY22b3Wk1MHh2YCddFrtJKDyqKhMSGTmAz4zhkWSUUiF3PjHYypf5U2lNNNUrdVU0J1rIRXaiTCEV+bkVRjym2b657P1OHcltE7BSaZMLqhAlNJ88mplCT7X2XRCKSw0E0SpwFIrRSokho3RGiUEjTjDocUWpodCJJkEISTPb45N1zw2mvuyr91zm3GGdiIxV4/RSnTwepRKMArEacfFlwziDmfwpSfn0UoE9k5U4HOGP//NMXz2o1VUhjN8z8/M4+VvaoJz4I6n57HQSHDrZVP5be3mSK9OdhBSCgC3X7VFZ/4xWjDj2PjgZIebNoqhe2JKEHKdAbh1uASfLcNnFNK4MhfD5B0cHBxOFVEqwZluBjaiDFGqt2jqUYayzxBwXX9S89nWrzz3W+8t4rVXb8GX7m/hsTtqmHr+EYzuauIFNwwDIGCUdGQsPby35DGkBb29q31nF84YMSWE7ATwNwC2QAenvUsp9c4z9XhnM4RUACUgSmf79TowSqWw0taT0MONFoZCD0st3bH/3GNzsAELT842MVH18diJ9cXfFqNlD4utFARan7RztIQrtg2jXIh9YYRg+0gp10y128Bv/Xcff/xHwKWXAh/8oCalgFtzcHB4tmDXZwkhOSHVmxQq157avzMzVVWGfGpySXPnbqtPjTNtVCQVkBoSGaUCnJGcRAqjbU2NQZICuvJAATsxlWaNVwJIkQhNUq2xTrlArDNDgG0Ga5QKcEoB6EOLkHpK2k41odXryCqPxRJCP56IdPSArafHliPUowyhR83z6pZNbIKXakOnBoc0dbE6LHHrN89j+AVPYvueEjjXWoajS3pd7BMPHs9va3X8K9HGRLTss648PkK0rjNcwx1yPTCq43M8RnRsEOte5Q28zvc+p/CN4Z41/XBruw4ODqcLjShD6NPc/TxK9QS1bczzmDkLZz2fJ0Ust9euobWQY/F4gH/59Z2oL1O89aVtXHtpAI9RQ0h1PdPnVpXfpm6eVybdlPRsxZmcmGYAflopdTchpAbgLkLIJ5VSD53BxzwrIZUCVYBQgE9J19pCZGILVqIMUimcWI5xaL6F8WqAew8tAkrHDQDAvYeWNrTtf9ElE/jCE3MAgPFqgMVWiu2jJVy2pYapWohqyHMSGnAKSgnKPkc7FYhj4JZbgIcfZnjrW4Hf+z2gUjlzr4uDg8NgmGvEiDI9RcvJp9LdZqCz6mu1pkWjIbvmK6UmcqnRj8aZgFKeMSHSt4kzAUJYvqKbFkhslEpQipxsAshJcpJZ53Dtshh4NJclJGYVNzaGF1IqtBI9Ma0Gep10Pk4wWvZzzWkqJBZbKSo+09sk5vCif3fdXc9M991OJuebCeJUIM5o/txOBvUlirv+YRce+c9pXP4Hx3H9czN8+48u4em5Ju49JPLfXa7xALZCN+ONA+C3DIV4aq6Z54wWXXM3C8/82wg4RTMW8Ho0pgGnuY5rtOyDGrM7j9GTfq0cHBwc+uHgQgu7x8vIhMJyO823ODRB1eZsqdGW2slmL3r9WIq4mG/Dr//wNLIM+KU/mcHeK7QLuiamIm8QMkKQGWK6b7qKew4uYSj0XM07i3HGiKlS6hiAY+brOiHkYQDbAVyQxFQqkh9kik6VqdBumFGqD2T/8fAJzDVifO8L93RW9QrvzY00UsXg3+0jIXaPlbF7vIR2KkGIXmHwGIWCyp13KSg8JsFLwPd9H3DDDcBtt52u397BweFUMVOPsdRKsXu8nOeR2nxPoDMFtTlx1tQoMuRQGuJKCMkvt3+3U6FrjTFLskTFan+UAmIhEGUiJz/54xZWfluJQJzqOKvxapDrWzOh8imrNW5qxBkWmgkCrp1jF1sJQo/lU9Ekkzi00MJENdBxOOZ2K+20K26laJgRmZibVGxMCPuh1ST41/fV8PH3DiFuE+x63iyGJ1NYqml7gvZA87nHZvveT9HwaCNMD4fwGMElUzXM1CNMVIOTeu6A9g+wzuoAcof1TGiyqtep9f+3yZp+HG2GR1w2n4ODw2lFI06RSU1K55s64tB6BygFBFwb0ln9vOWgSik8fKwOn9N8I8Xiqm1DGCl5OP5kGb/zI1vAfYX/8Wez2LJHT1YZ7Ugp7MS0GGlYCz2UzEbKeqTX4dnFM6IxJYTsAXADgDueicc7W1CMbWC041BZJJd2+qCUnmbMN+M8vgEYLHjd4vl7x/I3I6PI9aMlnyMRKRjVZkdbh0s4stSCUsBnPwv8wA8Af/7nHC99qcLP/MxpfAEcHBxOC4pNLasBtbFRgHazLWZ12olnK9Gks+LrqSlRWg+amtVbqYyuFMroQwXKPjNaVuQ2/lGqNZ4EyGNkAKCdmDy6VGCmHiHOhFkp1kZuNmdUOzKKnBC3zfpqYghrOxFYaCZ5085OWQ8uZDnRElLi7oOLSNeoiYNmOfeDUsCvft8WHD3g4eZbW9jxiifBxhsYmZoEoJ3qbAf+4EILN+waweKAmtH1H1fhym16LXjH6GAxMGtBk0xqpqAEnBIMhR5aSYbQY2ZCoV8jS0RtbJlzVHdwcDidqEcZMiFRj1K0YgFKCQKucolFlAosttI8MQIAHj9Rx/GVCHON/vEwQyUPU0MhHp8JURmS+KX/PYvdFwENw18pIXlsIe35G9BT2qu3D+dfO5ydOOPElBBSBfBPAH5CKbXKPpYQ8hYAbwGAXbt2nemn84wikzqc3rrqCnNwysxhL870dCHOBKD0IcGaL959YBHL7axPAtPa2DIUYqTs41VXTuOR4yvYNVZGKxEoedr0ghK9tlvyGeIWxe/8uoe/+wtg717A85yG1MHhZPBM6OmLBmjaMVeTN27awU1j+pOZ3FKrJ7X6RaH07SjRh4JWrCeg1iBJSp0BaqOnbAxMZtZ+9SEiwUQ10BNTk2XaTkU+QSUp8mlmZmJh7P3ZVd4o7UxYAf13PUoRZxKz9Sgnl8bfyXytr2snsptxqF0PrQbB5z5Wwau+qQHKgG96yxImtgpcfGWC/3w0xlKre7ulWB4fO14/Lc9hs66T/bB1JMSxpQic6glpwLUDL6MEk7UAx5cVaiEHZxSU6n8PzuzDwcHhdEEPVrrPj9a9PTafAUQpCKPrVAX9f1Ha9sDRtRMmrto2hDAtA5B44atbuPnWFiplgpB7sK4rOuqwswkCdEdeeUx7qgDYUBbn8OzhjBJTQogHTUr/Xin1wX7XUUq9C8C7AOCmm246L7a+raFEPiE1DouZ7GikUqGdMGfriXabJMATs43c3PoRc/DZzAviMYpLpqpoxhledvkURso+Mhmj5DOM+wFm6hF8TvFv/wb8wA+WcOQwwY//OPCbv+m0pA4Op4Azrqe3dSAREr4xMhISIERf0ogzjJS83OFQr7/qFd7Mak8Nu41TgWaS5drSKNWmRs1YTz+TTK/fxplEPcqgoAyBzDBS9pAZ86FGlKGVCDAbw5KpfJqZCWU0p7ozXvIY4kzmGZ/2UJIJhaW2duclhHQRwfx3Nz9qxWLguBegE+zeuR/9GkR1jk/8Qw3/9oEaWg2K6rYmLrqmhecV5Au2y942uaw6aqVzkHn4JIkpJcA3XL8dUSrwyPE6tgyFJ3U/RQyFHo6TCIHJoq4EHH47BSUEwyUPcSZyiYf9vRwxdXBwOB2IjBRkuZ2i5DMMhRyzjTgnn3nmtZGLWGSFJmaSyQ1Xax/5zwn89v+ZxC/+8Qz2XZPAD/QwpWgU53NdA4dKXq5ZLU5MGSUomQktdxPTsxZn0pWXAHgPgIeVUn94ph7nbEQmFDymCakNlOdU5wDun2/mMQpRJrDcTnOd1sfuO5bfx0bnr9CjxgGzg6ESz4PSOdUHlOV2itBjGK/4mGvEYITgsceAcpngHz8a47+89tQPRg4OFzKeCT29tc1vRFmXHjAPIE8EhkKer8DqNd3OCnCc6bxKRfR01RrzZFKZKaaOorKRMQDw0NEVVAKWB6ADyCeW1tCibUinXh8u6oX0/cSZyDvmkZmgAp0ueSIkkKBDTNdxpNgMKT223MZXnlrAzXtG8xXZe5+u4yN/PYIDn9+GqEVx860tvOl7l3FP6zBmnwbefMP2/Pa2mf41k0P6Dddv29T2Si9ec/UW3HtoCTfsHAGgnXevN1+fKsq+9g2w/x9Gyh5OrERgFKgEDFtpKV+HttMDt8bm4OBwKmgnOm7MmtylQoKmQMQoHjiyDEB/XhQJpyzU8PyzKZX42P3HsBaUApI7LsdH/noU19/Sxs5LOhIKSpATTUDXNUKAkNH8/ovElFNX984FnMmJ6QsBfBeA+wkh95qf/aJS6uNn8DHPClgHTBt83040MVVQWGolOv7AmJS00wxpZlwzNzEefd5F46gGvOsNPVzyUfIZQo+CUYqK/RoUf/+XHpZkgGt+iOBtbwO+9/sVGqcukXJwcChgPT39qcgWLF+z67CWnNrPfB0Jo111rUOulCqftMaZAPM1yYwLDa22yR5VRtcupEIrFV2P24yz/HCx3E7zOJfjK1E+yk2FAoHKn49QCkLo763WtBmLrhVe/fwVBLH6e3XanBJX2noyu9RKMVkiCEKFI/Umjt57MSauWMD3/kgbl1+pH+yee1bfnvaspR1eaOGug0sn/XxCj+H5e8dP+vYAcufeXngme7pkHIyHSh6okW5UAw5GOv8/7e/lZBsODg6ngmPLbeydrKKddoipZzKU5+paIxqZzxaL4kaM/TpbZ1IqBXD3ey/G/i+N4qVvaOD7/98FsAJrIQQIeYeYemYoQwmBgjJf6xXeYoSMw9mNM+nK+wXglJrM5yysI2Ym9QTh8GIbeyb0G6YZi3yakAm9YpdJtWb0wFoYq/ir1t5GK54OLjarDQFnOPi4j59+ewn33k3wqjd4+Nm3ETAGVMsUgfBP2+/s4HChYyM9/cnKFiKj47RYaCYYKXtd17FZyLZDbR1xbVlJMomSxwCoLnLzxImGjpCCyo2OUpF1njMUDi208g90a8ZmjZM8RrESpRCyOyC9SIpbSYZ6lGKpleQrpNaExzqPn27rfkKApUNl/Mt7d+LIgzX84T8dBSEKt/3ifeC+hBqtARha9/ZFDEJKd42VcXChdWpPfB0EHkVmJt2TtQCz9TiP/RoKPQyFes267Omc6mrIUfJY1zqz01U5ODicKpSJ6kpM3rQ0Odo6WqwjuVjoMTEqnnPtx9B6WzIH7pjE/i9N4c3fv4z/8gPLq+oyJQScEXhM53v7jGLHaAknViKUfI4TPAIhBLWQY6mVwmOu/p0LeEZceS80SJMbqGNgJFaiFO1Eu12uRClKHsMDR5axe7ycv1HlZsalBsXAdADYNlJCyWMoewyLywq/83Yf7/o/JYyNAe99L3DpLS14dCS/vufWuRwcTgsG0dOfLI4vR6vcua1W0yJOJeI0hoImjSvtrMskSBuuIZcR5D+XKnfSVUJHShUb2HEqUUfWtS4F8xgKusGWCLmKWBbL2UqU5Q6/VhOb9TyH0wWlgAe/GuADfzGGp+6twg8FbntzE8Jsh3C/M6nthZAKT801sHW4tGpiOgiGws7H6ZVbh7DSTrF1JDylCBhAr6tJpRuNzVjAYwTTQ5qYVgIOj1GMVXwMlTysRCm4WesdK/urdFTOfdfBweFksdRKMFzy9GZNItBKstw8jxD9uVA8V/Z+bvUbwPQr/0rp+9vz/FmURxN807dX+z4fQvR0VEdhKUzWAtRCjoWmbtZZh/KAcxBC3KbIOQJHTDeBXuexfk5kgH6jSakPXMeXIzSNA6bPKVqxfuPONxJsHS4hFQp37V/ERRObdx+aLBx4to2E8Cg13SEPX/wM8H/+mOPbvjvD774D2LGF44Ej1B1MHBxOM860nr5fR7k3GqX44b7U0i63YaFxJU3ES7+usSWtCljltmYdeXvLnNWdWsfx9WD1qEC3A+OZwNOP+PjtH5tGdTTF1W86gFd+YwM3XVpbdT2bBVssh59+ZAaNOMMDR1awwzg3bgY+p7hsuoaFZozLtqx+zJOFzykYJfn/T59r4skZwXjFh8cIpmoBAk7zQ+FYZTUpdXBwcDhZSKmw1EpR9vWGjY4Ii/P86MBjaCUCHlv7A6Ff+e8lqyvHSvja312M533f46iMx7j2eRGANYgpkG+NZFLBY5p8+lxLGwLOsH2khKV2dyyNw9kNR0w3AX3Y6/zjFlKB9xzyhFnLtdl+x5Z12HqSaXcy63oJAMdX2mglAoeX2ji8NHgouz1MeYyCUyCTwI27RnHv3QQPPwS8/I0Mt74iwcMPA2PbMwyZVa7QcwcVB4czgGdFTy9NLSnWJAC5KVpS6FZLpV1qgdXTwo0mlpac9uJkOOagK7v/fM8RXL6lhiu2rr1uCwArixSf/lAVaULwzT+0jL1XJPiJ355F5ZJZPDq3jKDS/0BzeKGFp2abuG7HcP6z4hQ6O4ndYkYJrty2/vM9GficohpwBNxKNCh8EwmzY7QEj1MEZnJgV3V3jZ1aJqqDg4ODhVLaxDMyEWE+o0iF9jJoJx3Tu5QSHFuONnXfxW3BEw8P4yt/fimoJ5E0OCrjMW65eGLN21KiNaShcX1nRlPqc2pisygqAc83GB3ODThiugm0E9FNTJVa9QLqKYImpVbnZX9+YiXC8eUI9xxcxG1XTOP4cjywCHco5FiJ9MGpGnAt6qbAt9y8E8ePK/zN747iPz9MsGsX8Mo3MgSc4vLLgZWI5YeV3gOsg4PDqePZ0tPff2QZT8028fprt/Zdy+8ljvb7Xh46CAc7w4POvnjkeD0npu1E4BMPHsdLL53EWMXH/kc9/Ns/1PDlT1aQJgTXvaiRr3/d/LI2Hj/RJ3am8LXNWz282L8heHyThysAp7yy2w+M6hVen+uMUk71NMAevKaHwkKQfKfGu2mpg4PD6YKQKpektZIMLPS0DMTEaVliyinFXCNedfuHji7j0RMNvObqLbjn4CIWWylee81WAJ3Poyc/N417P3ARhra2cMtbH0FlvFufaiUN9m/AegEQbBnWWc4e03ExHqUIOENoNkvGKj5W2s7t81yBI6abwFI7xWilYxjUL+ddZwMCc0294pAKvTLWTDIopQ9bUSax0k4xXg0GzimtBJqY7hkv41VXbUGUCjAw/PsHhvA3f1JFHBH81E8R/MqvAMqjiKU+mISc5cYljpg6OJzbeOjoMh6faeCN123DEUOqbDxVESvtFDP1GFO1APcdWcYL9o4P7EiolDJrUc8OuVF9mPKsOew8NdvAlz+0G+/936MIShI3vnIRwzcdwNCWCArbQGx/oM+v2q/WxtnqSfBmsW+qiqu3D298xU2CUeT6Ke3CTEApQcBZTlZ7a/pYxRnaOTg4bA5SKlBK0IwzVIIOLVhqJQg9BkoIGlGG2Lirl32uY8kyiWaSaY+DTKLi876NzkdPNAAA//rA8fxnx5bbmB4KcWIlwv4vT+Ke9+3FlqsX8a0/dwSHm8mq+6CUQAoFbpx/AU1SKQEYIfAYNRNTAmKmpiFn4FRvnMTps9BddTgpOGK6CTTjDJmQeTdad4lY189iod0qhVR55t9H7juKkDO86qopMKInFHZ9blA3XqsL2zIcglOCksfQmAnxrt8Lce1zY/zc/2jj2141CgBoxDRf+yoGqZccMXVwOKdhP+C/8MRcbizRz8/hPx+dgVTAZNXHbCPBXQcWcePukYFy3B44uoInZhp4w3VbTyn3LckkHj1Rx5VbhzZl099bEednGD71gXGIbSl2Pkfghhe1QSnwktc38O9PHO7cTmHTc+tGvHliunU4RCXgODjfROAx7DzJtdli57/fzyghYJTAZ9SQUdqlJ53sM6F1cQgODg6bRSPRkq8jS21cOt3Rxx9ebIMQYLjkoZkIs76b4ciilqZFqUAr0ZEwJJNgtLuetpIMB+f7O5V/5akFXLdjGI/PNLDjhhbiuodLX3EUQbkCNPV1pod0jWNUk88MWkeaGNWFNTRiVJ+R7SahrYMln+auvb0Gfg5nLxwx3QTaqXagtCTUTkwXWgmmaiEA7TxpL2unAmmmDBGV+MjXj+WHjkzo3CcbRLweCICKr/9XHX60hEc/UcX3vK2F62708M//0YQaXcFFE53DEaf934SOmDo4nB+Ya6zuKBfRseLXfx9ZaiPgFNftHNnwvg+ZyJPZeoytw+ubAC22Egip+q6xPnxsBU/NNTEUcuwer+Q/IwAuX0c7qhSgJHD8wRH8wd9P4J4vlaAkwdXf0IJ6ziK27c6wbXe9z++swAwztZNTO339xAPHTlpjtG+qisdnGvn3No/0mlOckjIzASiiOA3gzDpOmjxSSlDxOWqhB0b1+tqq+3Sukw4ODgY2WzRKxbobc804Qy3gaBZ09u1EmNxqhYVmAk4pMqFMRnaGTGrJmp2tpEIBSTcx/fKT87kEbdVjzgX487+cxI3fVgcPJS571dFV19k5qs+1NhYmzmyahAAl+mxMjQOSxzsTUytvCDjLTUHd+ffcgSOmA0JKbTSSCpWvPaSGmc43OsQ0ySQY0ZcV85yk6s7qk0rh4WMrOLGyeh+/COs6tsufxD/8zQ783y+PYGJK4r99f4ayx3DFlQqPz3S/6TxG+zqQOUdeB4dzF4cX+3ee14uaKkbGPDXXxJGlNm7cPYrRkgdKybrrul95agFvvmH7us/pM4/OAkDf61k34WLde+S4JpRrEdO7Dizi4EILn/7da7B4oIrhMYErX30UO19wAtXJGFBrE+Xi45xY6daItk9hjeuqbUM5MX3ppZMnfT+9YJQg68lwLU4DKNHTUp/pVTqPUZQDrZkiBBgrr17bdTXewcEB0LrQ5XaKiWqAepTlxLSdCJT8zqZfKvR6bmzOtxbNJNOZ2IlAyWPIhB7MIEEuTeuNg7G3T4XE8eVoTVJ64uFh3PGefVCSYOVlJYztaeaXFZMupof0udpujrRIJ47Grhj7XBNmn1FwSnMSC2ijODs9dfGI5w4cMR0QsXHVVcZxl4KgFQs0vSwXVWviKgGm3yh3PLWAUXN4kLJ7RU0qtaEbJgGQthke+tgOfPBzU2Bc4Xv/nxZ+7CclJsf0wZKYda/ihJRRgpC77pCDw/mEr+5f7Pvzrzy9gJdfPtX3MtEzkYszia/uX0AmFCo+w6uu2rLqNqeD2jw+U8dSS9fFjYZ4WQbc95UQd322jPHbj4BQ4OKXHAcPBd763SV85IEjAz1mURYxU9cNvyQ7NV0RIfqgdPtVW8xK2Ok73DDacdG1Bzp9sNK5spxS1EIPJZ/lmqmyz/VhixBHQh0cLnAIqVat72dG+8kpQdtMMFtJBkBvtRxbbmPvZBVxpnWhmVS4/8gSRss6n3SxmWC04mO+kSDNJBpxBkZJnjiRCYWVdmYeq/8Z9oEjy9jfZ4VXKeDxT23FfR/cjaEtbdzyw4+iOtVpIu6bqubn5Ku3D8E38gVLQLcMhQCBySalCDxdExtxlpNQSrRBKKA3UKwMrihrczi74YjpgEgymQfRC6lAINE2+/V10xVqG4cyAt0xenK2Cbss3/v2PbzYNoYW3XjdNVvhc4p/vucICAFu2TuBf7tjGq/7xhjf92Mt7N3NMF71c+2XPdz0HpjcocXB4fzA8eUIX3xibs3Ll9vpmpnKvdEwQEer00zW0FeeZOmYrcdYbqe4ZKqKB46sbHh3Bx/38LmPV/DFT1SwssgwNCrwgueEqE1H2HOLnsRyr3sSK6FwbLkNTikmqt0Tw688NY9bL+sm6IcW23jO7o1XeHeMllY59L700kkMlfRH5Gb1SYRs7HbMqO7s+4zlOa+MdlZ8Q49iuORhrOKjlWTwGEHZZPNRt7Lr4HDewq7gDnI9RnVtklKBECAx08rJapDHfGVS5fe52Erz1IhM6MjDdiKNVlTh0GILhOitk1RoIpoJvbZL6dpktIhojYbgQx/bgYc/thPbb5jHzd/9BHjYfb2dY2UcXOgmtNNDIRZbCTxGMV71sdhMMT0UQkhl6iFFnGpTOJtj2nEnJ30/Fx3ObjhiOiBiIZAYXWhmHG/bqUAzzrQrWSYwW4+w0s4g1cZB8raj3wuZUnz0/TV8/tOX49YfewSX7wnx7o/P4Nq9ZWSSYij04LFOkLp1I3MHFQeH8xP/9uDxNeuFhVTAkzN1LLdT3LxnLP95vwPCWj2rO56eN4YWq2/z2Ik6mnGGG3aNrvkcvmDI88WTle4L+tSmh+8J8BtvnQbjCje8sI2XvK6J625p4yP3rx/TMt9IcHRJX+cFRutpsdjqHwfw4a+v1i714tLpWk5Mp2oBZuqxieU6uS47o2TDAxynBEpp2UUzyYybpN6ASYVCyWMYLnn5YctKNFzn38Hh/EacdYiplY71QyI6OdaJ0DmeSSbRNBNEu2orpEIrFhguUzTjTMe+xAKMEShTp1qJgFDKbLq0cn0poM+zcSbB2cZ1DcCaXbk9z58FDwQufcWxVR8LL9g7jvGKj4WmjlEse5qebBspYbGVmOkpNQ06DytRirLPEXoM7URAAatIqHcK5n0Ozx4cMR0ASikT+yIRpxIBlyCgaCfCaIIkTizHOLTYRpQIHFxo6l38TUCkBE9/cRo//cvbsTTPMH3lElTigVOC8TGCSsBNjio1GU36DUcIUA25c2N0cDhPMUjPSSqFB4/qKeVNG0wIi3rLh46ugBDgiq1DOeHrB3vfNhZlvW5+76OLFPja50r4j38pIRumuOoNh3DZtTG+9+cW8LyXt1Ab0c/HdveL+MQDx7q+L2qavvzU/Krrf+y+o3lGqYUdGl88WcFMPc43XIoYLnm49dJJLLYS7B6vYCVKT4kA8nWIKaeafDJKQYmO5Qm41keVPN31P7ESI/Q1MbUh8j6j+Yqag4PD+Ys4Ffl7PZUSAe2/sZEW6mGcSfiMIhF6+slZ2pmYCoVGkqGdCiy1U73tl2bwJc2bb81ED1XqkW7wNeMsJ8R20NKvpjXiDCVPxxJ+/fASpFRdnwEnHhnC4bsmcOO3PYXKRIzLXnls1X0AOnGCUYLLt9QwWvYxXPIA6LpHzCovp9rkKPQo4oxi3MRjaU3p6npttaYO5xbcp9wGkGavXkoFKXXuXSL0LnucCTQifShcbCWYrcfglODj9x/flLZp5VgJX/jjK9BaCHD5DRF+/vcXUd65iIsnt4MzarRFDELqDCfGtBAcQO7U6LroDg7nJ8gAu7XFld2vPL0w8H0/ekKbEV2xjktuER+97xgIgDf1mB0Vs0ftl7OP13Dwjkl87OsTaDcYgqEE+15uyCdVeMU3NhClAncfWIFQCnsneiat2LxpUS8pLSL0GF5xxTT++Z5uzeqOEW2oNFrx85zq0T7GQptBsVHYu9bLqG406mgDrZMKuK7zU0MBhkselFpByWOohhxJJkFMhmnFEVMHh/MedrAhzLmziKLDrtWmz6xEoJRgth4j9CiiTEC1tRa0nQisRCkYJVpulmijo1YikDGFUPM/tGIBKfXabjvVsjXrGt7rh6JMJKKQCp986AT2TlRw3c4RPDXbMTFSEnj4X3fgoY/twNCWNpImR1Drb4Z0464RAMilDbvGWa5j9TkFJcibdx7VtXLnWBnjxg1eN0pXf1Y4w6NzE+5TDuvv88eZXo9QSk8ColSiLBQCri/TmaXAfDPW3SSzxrvRsoNICZpzIYa2tlGZiLD3igSv+5Yl3HRLitGKh9l6gG2jJSw2E4ScohLoA0onq0kffBjVnSRnhe3gcH5ikG2kTz18Iv/6+PL667CbwT/fcwTPMYcGi3617UP3HoVSwNKhCj4TzgAAnvr8NI7dN4ZrX9jAba+P8aT/JGzj/77Dy9g7WcGnHp7J76NX43m60TJ2t4wCQnYOQzbK5nSiSEy9QgSMvcznRoJBgaHQQz1KEXKGsYqeFNgsQUaJMWDq3JeDg8P5i0zozTxAk9A4kyj5LPcRmFmJsWtcx6jYSeZsI8ZI2ccjx1dw1bZhxEY/KpXSWaILLXBKUI90zEuSabfdjKl8qthMMr3pIVVummTRu5lrs66tS/lso1tqEtc57vzLfTjx8Ah2PW8WN37bUwhK+qzcD9tMc5Ab0qkUsIIOMSXQwxjOSL7SO1r28zrrM9rXT8Hh3IQjptDrCZbX9bqcxZnuTikoKKUQZQKZ0O68umjo6y63U7TiDLONeFVoehEioXj6i1N49JPbAACv+bV7wDyF7//VY7hosgJCdJh66DFUA4aVtnbcrfj666K+FNCRAqFH3Sqvg8N5ikEmputNCgfB4zOrc0EtHj3RWPMypYDlI2Uc+uoEDn1tHK2FEK/67/diaGuGa//LAdz0nU/hBZeNYPtICU/f07nd8eU2np5rrnm/ZwJ2uvDiSyYx14jPCCG1KOaJht5qYsqp1koxSlALOULOEHgUIyUfHtPmHXYKrbWnZ+ypOjg4nCVIhcSxpSgnnI04Q5JJlH0GpbQJW9M02PIUCGjPkoBTNKIMUSoQpRIln0AqhflmgkzoLFKbf91K9CQ1ZTIfarQSvZIrpES2hi+exRMmPuvLT2pfAUo6WztKAZ//31dg5XgZz/mOJ7HnhTMgBLhm+wjuPbTU9/5YYdBi/1joOqkzSblx47UE1YL23Mbh3IYjptBrEyXoN+dsPcZE1Qc35C9KJXxOIZV+w7WNC28t9PSKlTkxtBOJzz4+29c4BACyiOLJz0/jsf/YhnjFx8QlK7jitYdBmAKBnkJYh10b/1INPHAaI+AMhOjLfEa7hPD6TeympQ4O5yueCVJSdNHtRb/P+4VmguXDZfzb269HY6YEQhWmLl/CVW84hNKo7p6Xhk1cDLpXfYFTyxXdDG67fAqfekRPZa/epvWxxZXd0w07cbCHJG6iuxokgzLf2z8ep/CNvlSv83aMjSoByz9LOCUDNSccHBzObcSZRD1O8/d7PcrMppwmY5RqAiml0sabsXbSnavHGC37kCZbVA9U9BTR5l+3ClPQdiKQSYlEAEMlO52VaCcb1+ViNrZtiDaMoZKSAKHAdd+8H15JYGRHx2HXuqhXA4bnXjSOTEjce2gJK1GWm3fa869dVaZEb4kMlTwEnjb5DDza15TOGYCeP3DEFJ11CKUUmkmGSspQM8R0sZUg9CiUUlDQu/dLrRRbhsNce2qxFikFgNnHh3D/B/dg6ool3PDWJ3Hz84VZsdCrepx1uugeIyh5DCXjwGiNjihdnV3HCIHH3RvSweF8BXuWP3AJIVASmHtiCEfuHcPwjiaAWWwtVVGZKGHfbcew44b5NfVDUil87UD/DNYzjdBjeON120DJasfG0w3PdPGzWOTrcfaQZYlqYL7mTMsvQk/X+ErAc98AACh5LJ/wUkryLD4HB4fzF7FZ3dXmadpdN84kFBQ4pahHGY4tR9g7keJR45Rej8tIhcK8WaeNUwEhtembVCqfkrYL5nKzjRiUEESpQMOYwW0Ub7USpbjjqQVcu2N41WWtFY63/9gIRnZ6uOr1hzG5r3sD51VXTnflTFtjoxfvm8wlFgDAzBZJ0UOFmxpKiG5y2slpL9zA9PyBI6boOI3NNRIkmUSSSWRCgjOqVx6sENx0o6RMMd+IMVuPMVYJ+t5ncz7A45/aCr+S4crXHcaWq5dw2y9+HaM7W3hzwTjksi0ZHj2+gosnKnkeKWcUZR8o+/rgYjtB/Q6o1Kw4ODg4nKd4Fj9wjz80jLvuHsfRr48hbnignsS+l+v4laaK8OIffWTD+/jq/meHlAJ62nymV7w8psPnqwGHxyiascjXbznTUotayI1rJsnz+BjRmzEBZ116Kf289YGs8xhOW+rgcK7DRr/Y82URSSZxYKGFNJNQjKKZCKRCopVkaCWZ1lEqbWhUjzIstRLUoyxfq51vagJajIiJU5lLy4rEsxkLlHyKVCicWBnMk+DpuSYacYaneiQYM48M4c6/2oe4wTF9+fKq2+0YLaEScCilsGe8jIsnq/llPqfweWd7xaMUjBAzqCEIPAbOtBsvIwQ0v8xNTM9nXNDE1BYHOzGNUl0IEvOHMwohgUaUYbmd4s6nF3DLxWNQnOFPP/Mkji5FeM3VW/DUbAOXG1fLhQMVPPbJbTh89zgIUbj4JdqUhBBgdGdr1XOoBhy3XTGNkbLXWdmgBJyy/ABjd/dZny4Rp05f6uBwPuOZ/MCNVjws7K9i27WaTD7279uwcKCKrVcvYfsN89hy5VIeir7SJ3blmcJL9k0glQpffrI7MqaY3QecGqe3a7kb/Wy45GGpnWK8GkAphZl6rE08mF45Cz2G6aEQc40EAdckdbjk5QHxPtf51L11vDgVcK7rDg5nF6wZ0WYQZQJlnyMVCsV5QpRq59wD801MVAMIY1qUGefbZiJA0JF17J9voh5lSIXCI8f0dNIOWKyeXZtzrv1crMFSuoY/wYH5Zt44+/QjM9g6HAJATmRlRvDgR3bi0U9uQ20qwgt/5OH8jOszkq/52kkpIWTNHGxqYg9Dj+ZbgkMlDmYIKDWklJC1z7xrZb06nHu4sImp1MXBEtNUaDF5kkk04gxln0NIhYMLLdy5fwGHF9s4tNDGrvFK7nx5x9MLWGgm2DpcwkMf346HPrILPMxw6SuO4pKXHUd5NOl6zIrfPd3kpoNeCzwsy7QrRBgAyj7vuEn2KYJuxcvB4fzGmXyHWyfdY/eP4tgDI1jcXwOIwht+92sIqhlu+u4nEdRSMO/scjy0MQGvvXoLOKP48Nf1FPdFl0zkmlKgv4PwoPA4QZZ030Pg6VVdC7tmRqMME1UfrUTkU1qf6wNUwHX+aDsRqIYcIyU/d56s+PojOOB01cGqGA7vJqYODmcHLCHNpNr0+StKJcq+Jo8ln+UT1DiVOqYlkYhSCUaRR70IoyelBDlRa8bZmoTSNuY2iizsJa2tOMNKnGG6FqCdCNx9cKnr8mPmzGsnryvHS3jsU1tx0QtncN037QcPOo+3b7qGTCg8eqK+Ljm2KAcclYAj9Bj2TFQw34jBKEUl0OdlzjQxBdFbgu7ce37jgiWmSSYhpEIqZN4BT4VCkilEdv2hpovQcjvN7bO/8vQCthpr6yym+NrnRzCxbwUPD61g2zUM3Je46IUz8Er6+ldvG8Jw2cMXn9Cd/duumNYHl0Jh8xhBNeRIpcREJUAmddECOmJwoP9KWu86iIODw/kFOzkbLnEst099SplFFIQCzJd46vPTuOd9ewGiMLangavecBBbr1mEX9GPUx5LNri304+pWoBtI6U1HRyLCIxJxgsvGcex5QhDJQ837xnNg+L5KXTRPUbR7snGCzhDZDRcQKexyCgwVPIQZxK1kOfukVZjyple2905Wkbg6eajMIdSoH+3300AHBzOPthzm5CdNIdBYXWeNqc0ERIBoWgkGURuJJQi4AyLrQTHliOMVTwoBQgFSKVv10rWts2NN7LU7cFyO8X9h5cglDa1e8m+iTUbYUoBC09XMb63gZEdLbz6v38d1anuVeBbL5vEaNnHXD3Goyfq8NapYzbj2Teae9vEW2wmWntvGnecEhDzlPTk1NXG8xkXHDG13a65RoxKwEGFyielmqTKfKUX0F2lKJV5IVAKeOAhgXv+cTf2f2kKaZvjitcewtCWwxjZCYz0rOvWSh6mamH+/UjZQ2gsuReaKTzzhqwEDJT4qIYczTjLLbxttID92sHB4cLCaFlrcG7aM9aV+zkolAQWD1Vw4qERnHh4GPNP1XDTdz2J3c+bw9arF8G++wlsuWoR4dCzt5pbxC0Xj4MQguGSzvfs7dzftHv1OthULczr7I7R8ml5Hv0OZ6FH0UoohJSgRDcGfa67+QHXrpEjJR+jZT09VUrlNbwSaMJaNoct0icQ3sHBYWNIqUy+78mfiU5mFRdATkhTIXP32LXQiDNUg84xuxXrGmudbe2GRT1K83rTTiQYpZgzZkZF6YCdVsbrTEPXm5QeW26jlQhUfI4vPzWPPeNlxJnEbKPTgFxoJV1GbBZxg+Oe912Ew3dP4CU/+SCmLl1ZRUqBjvRkvOrj2h3D2LlOPeaUIBUKgdcxgwN0U85numYCelLMCNHpFY6Unvc4b4mpLHSji2glApWAY6Gp9T5Qejc/SgUSQ04jY3bUiDNtqZ1JHFvW4e9fefc+/N+7JkCoxI4bF3DxrccwvnftnD8CfZixz6QWaoMMpVhOTLXZEYdS+rqZ7GSVFjOdTqX77+DgcG5CKf3e34zWVKQEzFNImhyf+NXrkTS1C+LIjib23XYMw9t1A608lmDPC2bPyPNeC4xizaB1n3W64WMVv29o+s6x00M8N4LPKCjpXnmzOdJRKjFS9gEoeIyCEIKAM1ACTNR8DJc8zDViZEKBEr0VE/DuDGq37eLgcHKQSgEKfd1ZB0UiJALO+hoRWfTm2gMdotivNvXi0eMruHHXKOJMghKCpXYKIRUaUYZGmKEZZ2CEoB5lGAq9/HZKKbTSjonRZtB7daUU7j64iJ2jZXzlqYWuy/bPt1ZJRfpFhx39+ijueu9eJE2Oq990ADfcnGK8Noz7Dq82O7IvFyGky+ioHwKPIRUZAkZR9nWWs72PwKM5qbfGRwr9z/UO5xfOW2KaCImwT75nM8lQCThW2immagESoQXmzVggzSSSTIFTieV2iuPLERYWFN7zFwrTN+kO3dCWNq543SHsffGJPKdvPRBonSiM1bXPaJeRhdUhlT2GdiIQcrYqB88SUre+4OBw4UFBaW3NOtdJmgxzTwxh5pFhnHh4BEPbW3jBDz4Gv5LhkhfNobqtganLl05pKjpe8XHpdA1ffmp+4ysD2Doc5rqkIhghEGuoP3sPgb1nkNdevWWwJ7tJcEZy8xALn1NjgCdzIm21o4B2TU8yqZuHRP+cEILRkgdK9OWtRBhtmCau/SYRDg4Og2ElSjEUegPpFoH1p6JJJuEbF+3hcvf70g42UiFX5cTbldteE7R+j1ePMkSpxEqUIvQYVtop6lGKdiqw0k51/mcq0IizrtqnkyG6zYNOBidWInBKcHChjYML7b7X2eje7/3AHjzxma0Y3tHEi3/0YYzsaCFTAS6erOLiyQo+dO/RLsfffg1U2+Czq7uAblAWY2FqoZcnTNgNlNAQVV1bAUZc/bwQcN4S035rFpmQRmQu0UwEMqlMFIxCI86QmFXeJCO456sUf/avHv7j41OII4LbpuoY3d3Ela8/PPBzIOY/gZmYUqLfYMXnZXfrqSGglJJ8jRcwsQFO6O3gcMFCKk1KiweeLKHgvmZLd/7lJTj41QlAETBPYGLfCqYvX8qve9O3HFpXk7QWto+UcGSpc5ihlGDLcLjOLTQmqj5evG8ScSpwbPn4qssZJYBQKHks11xZF8de10aWN+WAm3eP5ZrSk0U/V11qNf89RzStEQVKnofldgrPRBjYw1TAdXyD1Zjm92Wm22MVH6mIQAjJjfRct9/B4eRhJ4uDkrX1DIoyoSCVHlYMl72uy9qpoK9EkAAAQ/hJREFU3qyL0z7nSLOGa4ljlIr8OoutFCWPQSqlByBRhjjTJFQavxIbS3hgvgWPEaxEKYRE7mMCdDvlrrVdsh7uPbSIp+dWp0CcDEZ2NXH5aw7jytceBuUmW9l8FnFGsWusjAPznceyl1kSSswZWK8o6wYgp9phNzBNPs70udeagxKCPOMZgJG6Ebc1eIHgPCamqwtXKhQyaUhoZl3Q9KS0aX524GmKd/zUGA495SEsS7zktU3Iy57C6K5mn0dZH5Tqg1DAaK6HsFbY2vZaf182b0YbB+MxClk4PLmcUgeHCxhKT0Tv/UIZ9/7Hbsw9PoTGTAlv/P2vgnKFiUu01mfy0mWM7WkM5KC7d6KyKo+uF8+9aAz/fM+R/PtBNe4vvGQCwNrmPfZ+rtxaw11GP3r71VuRSbmq1g2XPFy5tYZdY5XcEG5QFLvzFpz1IaYFuUQRNkN6aijAcjvNt1vsOm7gMcSZNIesjkEdIwSVgMPnFEutFKGnb2dD5R0cHE4OdlqpN3nXrnNSKkilo1Y41VPMIoEE9PBCSNW3aWc366JMYBjd71u7WluMGQw9vRLcTgWSTEvCyj7TTrftzGSSamPN/Ky50ML2kRJmVmIdFyj610s5wHj48GILW4ZCcEbx5Gxj06S0GLOVRRT3fXA3RnY1sfdFM32lHrZcckrwokvGcd2OEfz7g8cRZzL//1ILOVbaOn815AztRMIzmyk+p1BQOfHklOp8UjtBJZqoWn+Vis8hlWvsXSg4b4mpVCrXDtj1ilTqaelCM0EiBDIp0U4EvvwFhiymuPYWiZHJDBNbBG7/1hU8/xUtBCWJD927eVIK6M5RyWfwOMXLLptEyecIuM6FIgSYGgowXukEqxfjAdwb0MHBYSVK8Xd/yfG5v74JUATUkxjfW8elrzoCkRFQrrD3xesbIvWrJFdvH+5LTItTzF5cv2MEAHDznlF8df/iqst3jJSwZTjMO+Yeo3jDtVvxkfuOdV1vpOSjEbcRFoim1tKvJp6EEFy2ZWitX21dMEKQ9TBTziiQylXX60dMy74mpqNmmhJwfVDymL69bzKnPUZzQ46do2UkQprgeIqRsoea0Y45KYaDw6khldaUsj8ttWe9mXqss4KVAhESnNLcCdeSSJ3zqaNYgM76biYkmrEAajrrs9evpKgxbScC7VQgtB4lmUQj05t3y20OqYDZRtw1DW2nArGQUErXd6n0VLRXStD7eP3QTgRaSYav7l/EztESRit+X92nRdlnGC37OLLU7pIvXLVtCHcfXMLxh4Zx93v3orUQ4IrXHMYLLh7vyooeLXtYbKVdHig6V1Riz0QFj52o55eNlHystDMEHtVbgyaDFJDwGEUmZU5MWU9zkBKCss/yaTelBM4r7sLB+U1MpQKjumsmlYKU+mdRKnH4IMEn/57hw/84jCMHGS69OsXvPHcFjCv84h/NIckkVtop/vXetQ99lYDpArYGGCGYqAaYrAa4dMsQkkxiqhagGnIwQtCIM4xV/LxT5Vx3HRwcihBCYcu+Jq58/SF8w+0eHhEHVk1Eb7l4HF96cm3dZz8+tBZHCj3al5huGwnzieWO0XJfYnrzRWOrftbPVOTKbUPYPV7ucis/E6C9zkXQK8O9dZsZ+UQ9yrRsQunDoJ2QlgpZo0WNqZ2EFrdeKCVd3ga10E1JHRw2i15DIvu9JVIK/U2B7OpuksncnZYAUEoTzAQSAafITFSgUtoQU0iVZ4tGmTbABHT0Sj3OjN5Rv6+jVLttx5nE/vkmPEaRZjEo1Wu+jThFJhTmm9rp9tBCy5hadlZ/7XNbMfFfiRD5FLKoU/3MozPYMVrGJVPaRCg2BLgWesikxCcePJ7XnkOLbRxa7K8jtbhux0iXHONLT87hxEqMpOHhsX+4Avf95whq023c+lMP4tUvZ9g+Uum6va191ZDnsgUrNbtiaw3X7xyGVHo70epDbUNvqhaYGK9MT0wzLYMYr/p57JYFJcSQ2QJZdefjCwbnLTFVCoaQAkLpopNJTVLf+Y4A7/njYQDAdTcn+Oa3LONFr4xQb2d4aq6JiycrYJRisbV+hl8/kTdBR0zOGMGO0TKqIcdQyLHUSjFa8fPrTg+F8HmnYLnQYAcHhyI8TjGxp40rx5Zw6TVb8fj9ncMYowRvvG7bAPfSv06FnnaXXQ92OrpW036t6Wk/WB0ppwRTQ2eWlAKrjZMA3bEPSnoFlxGi3TKpXr0txSkI9AFpuZ0aV15NWqnRQ4Uezet0NeBYbCUo+wzj1aDvcyhGRTg4OAyG5XaK4ZIHBb15kQoFqWSu75RKQfVsQyizuusxPVlNhI50UiBQ0LnBQiqsqCzXmgulkJgtOs9kDZ9YifJhQZxJNOIMyy2FXeNlJOb7TCosthJQQiANSZ00NUAbHok86qSViHxt2N5nb9xLnGrX3geOLOPxmQZed81W3Ll/AYutFIutZZxYiVALOY4utdFOJZ570RgaUZbffy/W2nyxmk4LOww58mSABz8/jDd/3zJu//ZF+GEVvom/KuKKLUOYrMa49dLJPOrG6j45o6iGnolbFDmJtfejNwgpFlsEFV9HJgYmqzTkrHtiSlc/V4cLB+ftp6adksaJwsc/ofDev6f4+V+RCEYVrr4pxjf/UIRv+VaJcCzCo8fqWIg93PHUPJbbGR48uoJ9U1XUwu6Xxx6sLPrRSPtGJQC+9eZdGC558CiFzxg81l0oqgEHowSxORy6VS8HB4ciimYPHiPYNVbK3RUHdXjtPzEleOUV05AK+Nj9nVXbXgJqu9hr6ZzWmp72Q8nnSNpp1wHk2h3D6+bunQp6N1BCT+uYqsaVfbTs4/BiG4wQ1EKOTGot6WStoym1PgB2MloNOaTU2tdKwMEoRS30cNmW/pNRtwXj4LA5SKmJ3pGlNso+x2QtQCIkiAQML4WS3RPTVEi0YpH7dKRCE1OPElPTNDHMpEKUSVR9bnxG9GR1rhFjzAwNVtopkkyaqajAzEqU18GjS23UowxxpvWkHqWYb8YIDRH0mN6Ea0QZpodk4fl1nmtxmpv/zgqYWYnw+IyOHizWZACYqceYqcf593c+3R370ottIyFCj+HBozr6ZaoWYKYeY6igc589xvDo58dQu+YoLr6+if/1waMYmxIo+r9zRvDKK6chlULIGaaHAoxWfAyXfRCSao19QRda9hmU0lPhap7bzJBmEoToM6/HCCZrAS6arKAZC1CKnMRbUELcefgCxhkjpoSQvwDwegAzSqmrz9Tj9IMQCnffRfCJD1P83w8QnDjBMTKq8KZvBq56nsS1z0sxdXmM6bESji8LfOXpha5JJwA8PtPAc3qC3HX3qEBM+7xxmOm+v/mGbZg0eXYe14ea3sB221FyKwoODg79UKwZhBA8Z/cYDi5oQyKPn1rd6F2zJQAu31LDHYVDjy1xvdOJV181nTfUbr9qy5qrwUXccvE4FppJ1+NulHNXRJ/N3HUvK9ZnRpHHtZR9rlfLhgJNTM3BKEoFWonAaNkDIfq13zlWztf4Qo+hGui86dGyZ8w5XO12cDidSE12vNaAarlTJvTGm1QKy60UQinMNRKMVXwQQtA0JkPDJQ9SKqSZ0mSI6yxiYRIYCGBIlt5Um63HSDKJ2XqMilnZj1JtXiSVnm42Y4GhUBPZB44uo+QxNONMrxWrzvSznQiQQEsClMKaDbfYmCP14sRK3OfaJ4fQY7h0uoYoFXhytombdo8CZvVWZMC//2MN//iuYVCm8KbfnsPUUICAdw9O7KSzuPUR+gxopeBUSyKEVPBox/it7DMzFPK0qy6jGAo9LDQT+IyiEujaO1bxMV4NcGihBU7pqiHQZjK7Hc4/nMmJ6V8B+GMAf3MGHyOHUsDyMjAyAjx2KMabbw9ACPDq1yi84o0tvOp2hVJAcHix060SUuWdrH7nnTjrfqP2Esh+Z5Kbdo/h+l0jqPgMHtNvOJtd6q9xkHSHGwcHh35YrzRcPqAp0KCf8W+6YXv+9fSQXkuzroi9JLbsc5SNKmFQt9zQY9g2UhrsyfQBpQRyDYMQzuiqgyAr/OK+iX4JPIqSz8AZwXglyLVNnBKEHkPAKcoBBzdmHNNDATymTYzKPkPIGYRSGCp5OuLLyS8cHE4rMqHy6D6VKbSSzDja6mi/WGhN6LHlNi6aqMDnJM8DrQQMqZT6j9DxJJKofAU34Cz3H0mFxGxdx7lEdYFtIyVIqRBlAkKoXEcqpJ6OLjSTPPu0HmXIhISQJCeo7VQg8GjuBN4vGQJYTVjvOrCwZsboyWLHqK6zV28fxqXTtTxm65F7A/zV743i0JM+rr+lje/9uQVMbJnsex+hR6F3/zrnYGvQ6TG9fdKMRb4VYrOaVWAnnrrml32GpRZQCjhqoZ6YWumDrcu9kTyOl17YOGPEVCn1OULInjN1/xYPPQS8730KH/gHYHoK+PRnFJSf4D3vBZ7/XMCrCBxfjiAJx0PHWlCqk0O11ErXve8HjqysezlBJ6tUKf31jbtHtFujOfDYN1y/iamFW/dycHDoh35bGa++clofAjbI9Nw5WkLJZ9g6XMKDR5cx19Caeb+HTE1U/a7Hef21W/OaNFr2cM32YewcO3lCeTLozRsla2SNWmjDk+6fFcstpxRDJQ+7xysmQkLryQJTlz3Tza9HmZ6EmsxSG18zPRRqww9KQEFyr4BB16kdHM4HEEJ2Qg8btkD7pL5LKfXO0/kYqdAkkhGSTzpTIbHc1g62UmrToOV2itQ4YLeMO61QHjKhp6Ux02umjBC0U4F6lIGEBEJpUpoKiSiz+k89qFhsJWaNV29gxKme1MaZxEw9MlNboBlnSKVWrwIwppoCadapyc3egrQGNktK+2UxW1y/cwQTVR9lM/2lhORn0KV5it/+0SkMjwv8/B/O4+oXNNclgCWPrZrsWn192WeIM5FLHYDOOXa4pDdOrLNu2WeglGAo9DBW9rs+t2zmcy/cxPTCxjmrMf27vwPe8Q7ggQf0oeXFL1H4pm+RiFOgHqV40a0UnFIstlJEmYBqKSy10nxFC+gvGl8PvddPpcLNF41ieijAR75+HLvHy9oow+TaFYlowGk+feiFexM6ODgMgkrQTUZ7V1hvv2oLPvHgcQA6h9Re9uJ9k1hu6/B3v8dU4sX7ujvmvevD1hHyZHHrZZObrrUeJ8iSzi82VQtQj/of9Bi1k92eDZeuVV6C3eMVjJV9LLdT8ELUS8lnqIYcAadYaaf5ZJQV8kn3jFe6zETGzLh4o+aAg8N5hgzATyul7iaE1ADcRQj5pFLqodP1AInRi5Z8hnqUYqTk46Fjyxgp+5DGsGglStGMBeJMouxrUhilWkcaG6PLZpzBM9tq+nKBgFM04gzVgOnJbKZJKQC0kgxPzCQQUoESYKmV5FtzUknMN5I8H7UZZ0h7Jp9JJrvq3HJ7/cEH0BmSbITXXL0Fs/UYXzuwiJ1jZRxabGH3WBlXbhvCsaUIQyUPFZ+t2mwRGfD1r4S48UURRsYlfv4P53DR1RG2jnOsRLpGrjXZDT2Wf34EXMft2PuvhRxxQ38/UQ3AacPUSxjHYAVC9DZN2ecg0GSWM4pyoWZ6lPYdzLhhzYWNZ52YEkLeAuAtALBr166Bb7e8DAwPA+98p8JNL2vi2ktDtOIMmaRmh18hk7pLZoXsgJ5sFrtAvdqpzWC07OHqbcMoBxxvvmE7Sr6ekBJzWCyu6NrVsH5wq7wODg5rYd9UFRMVvfo0FHraMMLUGH3o6tQza9Gvv2ZoJSLvsA+XBo8uWU/PuRkwCoyWfYyWN3c7m3dnUQ04mmuQWxvOvvqxSe48TAnBeMU3TcNOPfYZxXQtRDXgiKjA1uEQvtGWEkJg79bnFLLwWWFf59A5RzpcQFBKHQNwzHxdJ4Q8DGA7gNNGTNuJQCIEPEHQSgSiTKAZC9RCheV2ip1S5aQwzgTaKc3NjjTZ1JPUVqKMTpKjnegzYWKiYJbbqY5tKZCygwst1EKOVOiczblGglR03H+5MVLS9y30qnGhRlp3X4u1ckmLeLBnK+/GXSO4++BS189esm8CocewY7QEqRS2Dpdw/c6R/PKdY/2L62P3+fir3x/Dgcd8/I93H8clVyd44a0CK20taQgyhkrAsWyacXZwQ2AnrRRxJjBS9qAUjEOyrp9ln4OSBD7Xjb2RsqfXd6G1p6ngICAIud5KIYTkpLZodMSZdj3vhTsSX9h41ompUupdAN4FADfddNPAR6G3vQ34oR+WEErh0eMZpFJ4aq6JiyYquesZITbAWEGpzr/0NJO499ASKCW4ZvvwwM/VhgsDHaMQ63JGiDHXMAcVIVUXESWEYKjU/+V25kcODg5r4Q3XbcMdT2lDItuVtnrL0GNdxNRO+HaNlRB6esWNrbP6tRY4o3nO30ZgFOjj5QFAH3DEGuu3a6EYyWJR8lmXZrT7uZKu65d9TcgpIdgyFGL/fMuQVH0AIia6wK6hBV5HN0UpAaVklRkH0L22a1/nXm2Ug8OFAiPVugHAHafzfpdaKZJMIeR65dZqS4Xs6ExtPYtTiSYVSIzZUWyIZyYU6lGKSsDQTgQacYaVKEXAKaQCFpop5hpJ1/ZIMxbwTTQNowqNOMuHGMWBRioUWqlYVfOUArJNDjpaPZEuW4ZDvObqLXhqtolU6FxV6xZMiN762Aizxxg+8Kcj+PInKxibyvBjvzWLi6/ShDngFKMVX7sIJxKXb6nh64eXsGU4RCPKMFz2sNJOMV714TMGSjKUfAalFDKpCTsjum7qrUCST6WV0jEvFZ/n0TyBp2uq3k7RNXOy1onWYpT09R1wjrwXNp51YnqyIARoGl3BfCPB7rEKGiY/KpMSiRAghKAepfAYhSiQv/3zrTyIeDM5c9NDIRYLXaXdYxUMhR5aiV4ZKRvDI0L0Wkc3MQXK3jn7cjs4OJwF4OZDnhq9ZcljWEL3ytg3XL/N5JQyeCwDZwTxYHKnHCXPmIQM0PXvRz7tlJbRtVfF1sJoxYcQqmtqG3oMjGoSTEDyNTGb/2cJYsmnqAZcE1NKsGVYE1PeU4s502u6lYDlK79F8jrdJ2e1XwPRZe05XIgghFQB/BOAn1BKrTLjONlNOAC5djQz5pSx0XxasmP/ANAyLWinXaG0yy4AQ2j1BHWhmYAQPcG0xLZ4vSJiEyEjOUUzzroac9Z9NxX6OieLlXaKWshBCMHx5Sj/+b6paq5pv3LbYMZ2vcgy4Nd+aBr1ZYo3f98yXv+dKwjLeq1WKV1H7ZSyneh1aY9RjJQ8BB7FTbtH8aUn5xFwlsdmAbpmJlzqzx5TBwl0s87K1FKps6EpNZFcUZbXx2rIcynbVIGY+oyCuGOxQw/OZFzM+wDcCmCCEHIYwK8qpd5zOh9DBx+naMQZFHRhaiW6k9VOJDxGICSgVDdJfOhYp47anKe1UDwc5SJvArz6qi2ohNplTCoFj+mYGI/pQ2PvxJQSAkVPw26cg4PDBQubp2nXSvtN7CzRsmtW+vvO+q82plh7yglogpdJCinFhiu9nHXIp30MViCmm4HH9HptCl2zOSEQplOvp5x2HYzh4skq9s+3kAqJasDzrZXxqo+ZegxGCLYOl1Dyu3VMBB0NUzXwui6z+qdBJ6G9mi4Hh/MdhBAPmpT+vVLqg/2uc7KbcJZ0akMihaxnYiqVNh8qTkztbVRhqpqITvKCzhft+Ip4ha02oFvPGGe63qWZxIrqbvjZJp1dazW/J56aa2LHaCknleuhGWf41CMz2DtZQbtHmrClTzNsEEgBfPUzJdx8axucA2/5pQVs3Z1iYou+f0qArSMlHFnULsZzjRiVgOOSqWo+yayGHKHHMF4NUPZZ57PDNAKp8WZhpjGq71d/Fvnm55nsNO94YbIKADtHy/l6bnEa6rYFHfrhTLryftuZum+LKBVoJhmacQapdJFom4npSpRiKNRrtlIBUqg8C0tsdNIqQCrdyXp8poHhkocfeslerES6YJV9hpLPkAhtIa7deLWpRiJkl/mGPgy5Q4yDg8PmYQkfNw6yttNf1JQCmtilZtrIiNb4WBJrI1U8RnUIgJSgxj2xuOpLCVDyOOJUIqayK6KlV3tq3RfzyymBkiqf0vYau1Gib1MkxZR0npvPdf0koPm6mJT68MRMPAshBNXAQ8AZhkseFpoxyj4HI/o645UAQF2/BpRgohpg1GbbAPkaLwCMVfwuPZM7KDk4rA2iWcV7ADyslPrD033/lnzaialUQD3S5zuhVO6em09MUwEqiCGueprZTLI8kiXOhF6xFfZ7CXsM+/DXj6LkMdx+9ZbC4+u/UyGxEmlzpWrAu+pYM84QZxLzjRhTQyHuO7yMEysRbrl4AgAwV48xXPZAACy0EkzVwvx3e8gMQvbPNVc1/Cqb2N6zeOiuAH/3zlEceMzHj//WLJ778jaueZ6ewlZDjkaUoRpybB8pYa4e65XdOIPPtQkRowTbR8oIOMtzXEseM07lJF99tsSUU9qZmJKO/EE3J0mX3IIxAk/qF7vXcM/BYT2c00N0K2S3gnSpgCjRxeR9dx7E3okKrt4+kl//zv0LJxVi7HN9kBur+Jio6XzUepShGnAwSnKnSx0J09Ev9U5MHS91cHA4GVBCjPus1vToaeTqD3yfU6RC5OuqZdM40wcLAo91uvpxpg8WlrharSg1pkE+p2CJXhnWmXRaf1UkqgQwxhY6z48RgsCnuUtu78RUx7IwLDQ70whKCEZKHmYbsV4LYxScqc6hyDT9qCGejOgOP2Nat1+PUpR81jE1MppT2yTcO1nt0qAWndknawHq0cbumQ4ODgCAFwL4LgD3E0LuNT/7RaXUx0/HnQtDRjUB1SyxaXQIloweXWrnX8eZhIKeYtoYmHbSERZYJ+0iCYxTiU8/MpNf/vhMHfumal3PQyrg4/drd/MdoyXcvGcsv+zfHzqRf/1Sk8dpz5VRKvD5J+ZQDThGy14uGetFLymthbxvHvRaJnQHH/fwgf9vBPd+sYTx6Qz/z6/P4eaX6cfS5kQMe8bL2D/fwkjJx0TVL8gdGEZKHqRS4FRL0Eoeyx3fQxOjZSUSSSYRcO1lwBjpihvTOae6nkaku94XJ6YODpvBOU1MtdBdF69Wotc92kb4LiTw5Gyzi5ieDCkFgH3TVUzVfFy/cxicUQScoUEyVAJe0HJR4wCnSaldZbCwOXwODg4OmwWjBEMlL8/XZGat1RryTA8FmFmJUQs5mrHW1xNizX0YZuoxONWk0DbzKBHwOTWrcwKMUggp8/zOgDNwliHOrJaIIRNZ12GJEJ2LqhSFUsKQYZ4fHHsnpvb5ACkmqj7mGgkoJRgpe1hoJUZLqn+3gGtSbcmq/bnHtIbJYwQVnyPg+mBliSslwPaREiaqvsnP46iFHUdiAtK1NrdWvrSDg0M3lFJfgIlvPxPIpMwbZXaLw8oEbE2px1nulNtKMmRSIeTWoEd1EbmiMZyFVN1RLg8cWcElk1UdE0OJNkoqXH54sY0bdklwSlelOHz2sdmu7y2JbsQZGpsQ9nfWXNGla+WF7Rj7c6WAP3n7OBZOcPzXty3h9m+po1wGbHpNwCl2jJYxWQuQComyzzFS9nPzza1DISgletps13E5xZSpidYrxQ5dpNReLEvtBJySfLJLC8MXRki+sZL/ThTwiautDpvHOUtM24lAksm8I9ZO9GGrmCMlFfDJh04gExKvuWbrpu7/pt2j+NqBRQBat3TZliFUAy/Xk2rjDA4pFUJPd+ZtV5+avf3ihJQW7LIdHBwcNgNG9fRTW+/rg4w9UBACvPbqLVhua/nCiZU4J6+KACNlTxNZo9+0UwmP6XgrG39gy5PVsQYezYllJWDglKJt7tMeHglsV5wYN0v9PNuJyFdpi/A5RS3gWAkYaqGXh9CPlD1wYz5EiW72+ZxCQeVrZczqoQKt7Q84g0f1axB6NF/bpYTgiq1DueSi1+COEOQ6M8DFdTk4nC2QEpCym4jml5maEyUCHtfv2UYskAkJr0LN9TtE9MRKhErAMVuPc+J3bDnCsYLhkMXH7j+GVChM1QLM1FcPMD754Am8aN8Ellprb1f88z1HNpXyAAC3XDyOLz05D8Bsw3CCdtL5HTxGoBTB0gLFh/+2hm/43mWUKwo/8j/mMTYpUB3W162FncSI0GOYqgUo+xxDoYeyqX87TWaXXcW1DTn7t81mLnksH6yUfa6zYoNO4882+QiQT3mZIam9E1MnjXA4GZyzxLQRZ2inItcStFPdRYt6Qo9t1+rIGisVa8F2hQg62U2VgKNp9vOtgYZSeuXMHuZs58gKwi3c4cfBweFkYS33bdQJpQSB2czwGUUp4KiEnp44mgNETmAZ1d1rq4E3RiEepxguefn0gJnsUEa1tigwek9AN+cY1c22osmunpgycKpMgH1Hd2+ddC2so/BwyUM9yuAxgtGybwLvvbxDTwlByRhwDIVebl5iD0Y6tJ3lB6qAU4Rc/+yqbUOYbcQYKXt5FENv9IBd+c2fl2sYOjicFbAGlsDqHFD7fSYViJk/2EgrIfW0NMm0GdK9h5ZwcKE18OPaqWw/UgoAUSbxHw/PbHg/9x9ZHvgxAeQ+KHsmKhg2eaHtpBPtErcoPvjXNfzL31aRJASXXhvjOS9pY9cl3QS5SExLHkPg6RXdcsBR8fsbutnzqV23tXKHkq8bftR8Dti6TQB4lOaNPkpIbhbXj5i6uupwsjhniWnTrFvYNQe7yrtWvMGd+xcGvu83XrctJ7SU2uzAziTAL0wtCLGmHPqASKh9w1P0moI4ODg4nAzsxJMRkhsOaekAMF71waieJAbc5sbp+qMIcr2QNQ/yhHYg8mhnjQvoGBNxSnQAO2d5xqdtvjFKQYlCJjqkT2+HUNC21qZWA45WLMz0s1P3JmsBQIDhkodjyxEI0UQ0FQrlQBNNbg5HIaeo+FzrWm1+nvm9Sl5ndRcwBkaUYGoo1M+HdjL2+oHA6f0dHM5GpELmsSyZ7B4yiMKOq52m2h8ttVLMrET40pPzuGiysilS+mzh1VdOo+QzvOKKKVxiomKkUlhsAUIAH/mbIXzwr6uoL1O8+NVtfP+PN+GNtbF1JMTx5ahr5bcSaCO4RAhdu5mupxWfoWxMjXqN8iysHMQSybLP89pJzNaLrpkkJ7yAJrT2XFsczDg4nCrOWWLaTnWGqX1z1qMsd1Tr1QFsBpwCPieowLxZKe10i0xGqT3o6T8qPwTZA57NGXRc1MHB4XSAUZ2tKSXy2hKag8DW4RIWmgkCrqeMjHa64cyYJdHCKm9KZeHnnYMJgdUJGVLKBULeOYTYg07JY0iyNlKhQAAEHsu76qHHUAk4GE2wfaSUr9Nyc3ARUiE0007rxmu78nbLJOAMocdQCzl8Ts2acYdYl3zW1Y23Ae3jJoje1mF/jY59vxVjBweHZx+yi3z2XFZY7e1nCHRsJYIC8NRs8ww9u9ODsYqPl146mX9fCz2UfY7Ao0hihVrIUY8yPHhXgCuvT/HdP9LA9Tdo+cWxZYKdo2Vt8KSAepRCKr3NN1rxsNwynidm1bns87werhVn09vAC3ln20+fbfXAhZsJqt0mLBUmsIzqzGg3gHE4HThniam2DO983zCCeFUITz4Z2APY/9/enQdZVt33Af/+zjl3eVvvs28MCCRgBBIaEIUikDBYIIKxbFxRFMdVtirI5cJ2pVIuK3FltVJWEpVLcSJZkVVYkVMyERFShITA1mZhEySkArG5ECPEMgyCWegZpre3nfxx7rnvvtevu19D99x3u7+fKoru169fn+nh/bi/e37n96vFAUIteMue0fTOv++0a5RKT//7N3CQjHHw80s1ug+xExG9VkoEtSjAqflGumPqOtG6zrSn5hvpzTEfq/zNa/9YkCSBC02VnlF1O6+CSuS62vrENtTujGmUXHxERmO8EmC27hoc1eIAJ2bqCJJO5KWkaVFsNKqhgdGCs6bK+PFLr6ZrCI3CQsM1V6rFBs22RaQFzZZKd4Bdoh2nZ199Mgr4C6jFnR59DN6SDG4PenYAerHEjKh4Wm2LhWY77RQLuLhik9mmeSek2WMRyzl3a3XRYwtzgi/+eRmf+0yA//aXryAea+PP/mIBJxsLEAAjpRBta3Fi1nXP3TNeRr3lqveOJ3NJmy2LclJl4mNgNlZGS3TI7T3qkI2PYVLFIsmfT0RQSxLT7OtpcWO65hvds1mJXovCJqa9fAnvUy+fxuPJrKjlXLZ/Ai9Ozy1q561Vp+zthot24qypMnQy58+f82qpTjmdFySjCdrWpm90VjUQ0VrQSRmVqnfOmPpGQeXQpImo33X05Vd+NIpWSYxKGgkJXDKqRBBqVwY2V2+hHGpo5StDdFr+5ZoWBahEdVgL7J+q4MRMPenWq9JmRaVQIw5VOtbGn2vyN/waTdfdcrQUYHq2gcgoNFqdxNkPegeQzg80qtOkIzI6nQvYK/uzAJ7rJyqyVtvi1HwDTx+dwUW7R/HDZ1/Biyfncd2F23HP426Uy/veugvPHJ/Fw89P57rWq87bghem5xYlpgf3jePh56dxYNcoIqNwfKaeVngAwOyM4NtfGsHdn69h+oTCVdc0EYrBRCXCrqkI9WNNtK3FWDlALQ5wZHoe5dBg35TBi9Pz2FqL8fwJF799t3Rrbd9O469lNzMOFKKG+/+EnwXtXyf7elovPmNK9FptmMQUcGUggySlgLv7s2ushHrrWNcYGa2QHvoG3EWfks7heD9MWJJyOp+E+rNd2vKNSURra0stShPRyCg0W25EglaCStoIyO0mGuUSV3/zzHcRD5JRMz7RK2uTft3NqWt1mr4lPycyOv3YaJcMt9oW5TDzuHJJZzlwjY/8/DqTNDNyHXSTGc9JSW+QKdtttCyUuFl+O0Y7F23+zr0vyQ20G5kzqKVKeYlo+PU2MPLddBs9Z0+fPf76d0qzu52uqmTxmJnl6GTTolctNrjx4p3p59mkdGFe8C9u3olTr2i8+5omfu9fNnDxJW3MLCjM1F2X8olKiFPzDUxVo+SYhEYlOcowW2ph+2iMsXKAamRQCjROzjUwkRxpWAu+DwCAtN9AP/7PzsSU1sKG+j/3/YeODfxc//a5/OxJvO+tO/HGba68QvmmIMlFjW+0MZpcEJl0NyIZei+dTmb++4mI1pK/W62TEl6l3IWCJGOosh0WtXIlsW5AuntOtlN4mJwhqiQ33fxup4gbt+J3W7Vy3x8mO5++W2+QlPn60mBfhlsKO02JfAfgbAfdKNNoyegkIQ5Uuh7fVKNXp5RXIVhF1yKedyIqprl6qyspzX48k5kP+rNTi0e/DOr8HbX047OnKunHgySll+2fwM9fsC29jlTiqkimqp2k8PKzJzBW7k4SXz6ice8X3LVmFFv80gdP4k/vOIH/dUcDb7vU3fAbLQUYSSpHRkomjb8AMFmJ0ht2Y2V3TerHtywXQ18PpZBW4yzFdxdm8yNaC4XeMf3R89N49sQsIqOwpRbh6On6ss/fO1ECIHjuxGx6Iecvqi7YOYrnTsymZ6X83aFSoLtKcrUSGKsQZDqS+ef5rxMRrSUfj/wdbKN8Uui+7kdauYSyczGRzpnTku5ShlqhjjaipONtHCTzUQGct62Kh56fhhLAwpXzjpQC1GKTltIqabsZosmZVt/JtxS40l+VlPG6dSmMlIL0/JOf0edv8EVGYT7Z0S0NkJjqJTrtEtHG4Ut1+3ng6c6EhQd+chyDtvLYORbjyHQnkc02A9ozUcZDy5QDv/fAdtz9WGdNu5KdzzDZXS2HBloJ3nnuFjx/YhaBUdg+0jly8NyhAHd9bgQPfLMMpYCDV81hclsL1958Gnsnygi0u0HnYqhNjkEoiAQ4lSkP9skosHj8y1KPvV5aBC3YZc/mpxszvP6lNVDYxPT5E7N4+pgr4Zitt/Ds8ZXbg2+txdg5VsI5WyqoRq75BuAukpoC3HjxTly8ZxQvvDLfubAy7iLLj6XRSmAB7MyUnAHoW9NPRLQWBP5GWqdBkU66fwOue3jbupJYX8kh6Nzl1iIIkp3JwAja1jUqmqu3EAcq3VkVkXQXFMn3VyPXzMjPGQVcIhwmF1OSjJkJtEq7+PpEOtAqmVvaQCUyabmc0a4E2O+WAt1dHrP8LulEJezq2klEG0ez1cZ9Tx3Fd55ceV6oN0g0OGuyjDfvHoVRCl966IX08WxjIK0EV547he8+tbjqzo3P0rjx4h2460cvdn3tnedO4fRCs2tDYs9EOf346Isan/3YBB7+uxLichvXv/9V/MaHGpgPO02CfNXIaOaYQqhdPI6DzrgXAOuyI7oSoxVsb4tkonVU2MT0+Mzyu6P9tK3rmDtWDlGONE7NuZIQ91iAahQg1Do9+wQkpXOBTi+IXDMONzswq7dTJBHRWvFVG35MlVYqPSsKuF3FetM1XjMq6aSYOQPvm7NpEbSTJDUKXALqklGdlqWZZLi6gkAp1xDJ/6zQKDTb7aTMVyc7qJ3SX3/H3A9hD5PEtNmyaRmw+xnurL4/n+r+DEt10U1mmyadeoloY/q7Q8fxyuzKnW1X4w1bq2n8EnSS2d7S1GwCmHXN+dsAoCveerU4SEtpvfoCcOJlg+17mqjU2njxWYNf+dA0rv3lV1EZsdi3vYonMxvCgVaL1pLdeaxk1rUeO6KDYCM5OpMKm5guNSx4UJXQpImpe9MJto1ECIzvKNnpPJZtT26UoGU7F15ERCJyHYD/CkAD+Iy19qNr+vrJv5USSCuZlaw6I6lCo9K5op2d1M4sZT8aRmtBywrCZGdUJ6MASmHnyEL2Gskol8D6HQGjfWMjhW0jcXrUwQ9i9/zFVKAF1chgrt5yZ0wzJb6+mVP62ktc/GSrUViZQrQxrdcYp+w4lLFykCa+vc18svHrnW+YwvRcA+PlAOVIDzT6b/q4wjfurOGbd1YxNtnCf/78SyhXLT72hReRzWn9pofR7kxomHRKX0r2iENeiWnvSBmi9VTY/8u3+k1YXoIfvO6/xXeABDoXVL4JSKBdM5Eo6YCmk9Ex/myTL0HLK0AQ0XAREQ3gEwCuB3ABgH8sIhes8c8AgLQTuE52P/3FVKTdjqePZz6m+ZJcfx7VN0XyzY4C7T73HXgBNzLLC7R07RT4ec1aCUbLASarYbLr2p00KtWJl1p1Zq76we9aSWYcTKdsuB/erSfaXN5xzmR63bYaW5NZxlnZ8HHdge2dmNlT5eYrM3aPlTBVi/CGrVVMVqMl44/P1X72TIBP/YcJ/O4v7sKXbxvBuQfq+Kf//JX0GtGHz+xcacBdb+4ZLydHJZa+FGffEtpsNnxiun00xtvPnsC+iTL2jJfcuIWg+2594IfS60znSq0wVY3SC6xsN0gGCiLKuAzAIWvt09baOoDbAdy0Hj/IJ4EmTT473Xh9Wa5/XER6EtNOQusvysK0tFahHCV38jPxzfS5m++/ZyQ2qCTJpe/E24/fUQW6k9cwaSDnL8qWapyxXjspRDScxish3nnu1KLH37JnbNnve/v+iUWPCTpxpRRoXH72BCarYTptYf+UOxMaaoXrLtyOS/aNd31/OTSLZtLXFwA0XLx89skY3/92Ge/55Rl87H+/iH/9Jyfwlrc3UOrZkY0Dd3PQx8nIKLxxey3dJFkKrzdpsylsPaqfK7oSgSuduGTfeNql0peRjZQCLDRdidkC3LkpEVfSW41MevEHMDgQ0ZJ2AXg+8/lhAG/vfZKI3ALgFgDYu3fvwC8uyJTypmdMk8QzOTEVJU2IgJ6RVsm1UWS0m7OclABnS2r9940kF0i9sa43MUxv1mXmiwqQ7ob28mdQgf5n8bkjSkRZPr5llQK9YvOffjexsi8TGoXd42XsHC0hMIKr37QVgGsqFAcKzXangZxvjlmLDU4vNNCygH61iof+egL3fHg7/tEHZ3D9B07hmhvnccXVC9i1zeC5E00EOkBkpKuqTgkQG41myyJIYqCfTVrnuXmiLoVNTB994eSqv2esFKQXYnGgsG0kwpHp+bSBh9+N2DlaSi/i/FwmXjwR0RL6BYdFd86stZ8G8GkAOHjw4MBnEXxcApLdR3QacaQlvl27nD5x7ZTy+rv1vpQ3m1xaWAgkbeLRG+uWin3+caN9WXD/nc3s+vvtqvLcKBFl9ZsHf962aleprpLO8aysG968A197tNM9N9uPxI9lqScjXuYbdWgRnDVVwexCE6/MNtLuuKcXmrAARkoBvnoXcM8dVTz6vb1Q2uJtV87h4oMtjJYCN+YlamOkZKCV+xnj5aBr6kMUuJnO882WO3cfm7RUmbM/iboVMjGdb7Rwet41Lrr+wHbc99QxnM4MXQZcA492ciPKKHf3qxwZ1GKD2YUW4kBjrBzi2Ol6cr4qKYVD5yJPRNIdB85nIqIlHAawJ/P5bgBH1urFszuOPuHsN88zm2z670nHySSPqUz5L4D0Ik0y5+4X7ZiuUGLrzql2n03tXn8nc++XmLIahYgAV6p7ZHrxDNO9E2XsnSi7ruNa0GxZvOMNU7gvM94lPatuFK48dwqhcccQKpFB21rMN9ppA7ZWu41KaHB6vgmjBePlAI1WG0a5G3TV2OAnz9UxNmFRiw3u/UKMF54xuPX35nHZe04iqC3grKkKtArShnPVyGBrLYaIK0V+db6JOFAYKwdotYGzJit4db4JJYLd4yVUl4i3RJtdIRPT7KiYONDod8PdN//QCun8vDBpy90KbFpmERp3J8vtmHZ3H8vumBIRLeFBAOeKyH4ALwB4P4APrNWLS6aWtzPepU/JWvJvk5TBuR3T5DHdSUh780e/C5s9b5q10hnPQKlluzZmz7ryBh8RLeU9F27runl1+dkTaLeBXeOdufHlQONUq4nRUoBztlTw7PFZXLp/AmOlAKVQYa7exmTV7awG2o3CGikZnJxtIAo0ItNCvaUwWQ1xcq6BQLvmbJFRaDUE3/16Cfd8Kcb9f6vwtftPorbb4Hf+8ASmpoC37hvDM8cV2jZGHGiUAo3TCw3sGI1RCt2oQaME4+UQ8w23ozpRiTBXb2Ek2V3VIqhFJu1czmo8om6FTEyDRW/k7s+1Erz/0j34xt+/jIt3j6EWu8TUd6v0M/3iQCPSCqVAu4u5rmPy6Op6SUTUj7W2KSK3ArgXblzMbdbax9fq9bPNg1SSbPZNTHsaDKnMOa1AqTQh7b3ZJkBXeW9v4rrShdMgySYTUiJaiUj32dIdo6VFz7ninCkcn1lAoBUu2j2Gi3aPAXCbEPunqnjiyCkASJtbbqmFCLUbyxIHrjputi7YWotwZHoOpVDj+M80/ugjZdx15whmTivs32/xz35nHuXYXR9ObbXYOVaCiCvtFbhNkVpsMFNv4uwtVUzP1qGSGdGlwPUyGSu7JkvNVhvVyCRTH1zDNz8GhteYRN0KmZhuHYl7Huk+aCAAQqNx/YHtaLRcKUYcKNTipKFRMrNPi2CsHCIOklELqvugfL8D+EREvay1dwO4ez1e2x8xADqjYvqdweqU7S7+usmU/vZ+TTJJKdBvx/T1x0BefBHRWiiFGrvD8qLHA62wf7KCH7/0KpS40tqFptu1tBYoJ+P/3FxmYHY6wEvPhbjogEK5pXHXFwWXXz2HD/6G4Ff+YYwfv9xAaCTtPzJaCqCUYKLszoaKuGo8H9n8pIcw6WNilKtsKQUac41WOnrL/dOJh7zGJOpWyMS0V+/w43O2VGCUoG0FgEtMd42VMFoKYLSg0bLuYk+5cTL+wstVzGXPXzFgEFG+ehNJP3e0l3+WP4IwVu7MAQy0QjtpxtG7e9mbM/YmkcESZ0dXg0ciiGg9hUZhtBwgTirgfGntVDXC9GzD7W6eVvj6nYIv31HFQ99TuOwdVfzfr7awdUThkUNzePrEqziwaxRau6ZJRrmz+X53FADa4uc5C8rJMTDAJaZB5miYe451kyAWJG0QF2hW4hEtp7CJ6VVv3IKnj84sevymt+xEOXTD4hutNrRyoxImqyEiozBRCfHKbANaBFaA8XKAuUYLAkHb2q4d096dAyKiM63f2fe+d9kznW/9zmrX6yw5KH75ZkdrUYbLCzEiWo3IKCw0O6NUxsoBpmcbafOjLBGko2RcGa27eedv0M3UW/gvHwnx8Y8LFhYC7NnXwh/8AXDpNfMYK1fc69c0zElJRm9JmmCKAPunKoiNxkKzDaVc0hpo9/rZ8FmNDCYqIZS4SpPIuPJhf242NApGK54rJVpGYRPTN+8aTXdKz9tWww+efSUtYSsFBqVQo7VgocV1RTsxYxEF2gWcQPcvXesZJxUFTEyJKF8ii8++95NtMPR6LnzW4zwor8OIaDVKoe5KTCuRwfRsA3GgcbrVPYWhFhtMVlzDozhQqIUhHnxA4dt3R3jrJ10TpP37BbfcAvzizU1sOXsGb949iieO2DQ2RUal42QAdzMtTLqZT1ZChEah0WrDJmsTLO5yXo2Mu75Mdkf9v30DuSjz2kTUX2ET06w9E2XsHi+hEhnM1luIAoVyqDHfaKXtv1+db6ZBobcMziQjGHp3TKvRhvj1EFGBZZsfAcvsfCb/1iJDV+2xUmdfIqIsP380SI5f+V3H2CiczjyvHGqXlFrBt74FfPK2Gv7mr0IcO6pQqVj89m8C51+s8KEPuec3WgrPHPNzoDsVI6FxO5k+0fTNL40SjFdCNFptzNYFYl0VnpdNMqOgM5YrUAqRcRUsge78DIDVeETLKXTm5a91Wm0XXEZLARaaLcTGtfEWAQLjg5vq6laZ1Zlb2n3G1LfzJiLKS3aO6XL8BZbrPM478kRUXHGS/MWBRqPVRGjcBoJP7iqRxsmZFmBDTFZDPPmEwi/8HBCXIlz9803ceFMTV1/bxHm7y5hZ6CSCXdeCyZhAILmhlzQuApBOcOieDw3YntCarU5x1XjJ49qdTVXJBAggM5KL8ZloSYXOvHaMljCz0MR8s4W5ejsZC6MxWg7SzpU+EAxyobaoyQjLLYgoZ37swUr8M/ysUyKiIvFxzihJE9A40Hh1vulm1ovANhUevj/Go39bw33fCPGuaxv4xP9o4cIDwFe/Cmx70ylsGw8wUgowW+8klf1+Tvb8vkpu6KnMDb7euCsigNi+rwW4ndT0z5BpcjTl56qylJdoRYVOTMuhRiUyaFuLnx6dQSUyCI2bHdVu27S+H1gcmPphqCCiYRMHOr1IW072goqIqGh8iWxo3FnPyCjsGI1x/PQCxsoh/ucfj+E7d1UxOyOo1iwOXjmL636hjUAbKLG44QbgqZcUjBZUQo16cka1d2PCx9PeG3ihUZ0d1D5n9d0c6e7HsvE2G6f7dTNPd0wZo4mWVOjE1CeiAPDiyXlExnU/C5SgASQdeQdPTJVIz0RUIqJ8+TKwFWUuqIiIiibtYmsUDv/U4OtfHsEnHyvjt/7jaYzEBqNjDVx30wIuvWoev/pLJfzw8DTO2VLtmu3sS3VN5mznUp3He2Olls7r9Ks8kUzpb/o9S8TbfhV36RlTnrknWlKxE9PQoBJpzDfbCLUgCjSiwAWklm1hvBxi20gMYMBSXiXprD8ioiJh9S4RFdkzP1G44xMTuO+bIV541l2eXnCghYVTAYxS+NXfmsGO0RaUCMZrGlGyq+oaDrnXiAKV7lYulTT6xLC3w7lWkiaU/XY1+58xHTzJDJmQEq1oXd8lInKdiDwpIodE5MNr/fpaC8bLIWLTmSkVGTfDSuDKeEvJbKtBz1zxXCkRFVFviRkRUZE895zg//xFCbv3tvHvPzqPv/reKXzt2/PYu8c1JZqshAi0wkjJ9RHZOhKn5zpN2rVXQ+v+Z0s9v1HRm1Nmn98vqTVKLWpEN1EJB/7zMUQTrWzddkxFRAP4BIBrARwG8KCIfMVa+8Ra/QyT3N3yZ7CUdAYfi7TY+YyINg1GOyIqsne/G/j+E6dRrQH7Jit47kTTNbFMdkUv3DWKI9NzqMUGgVYYLQWIAoVS5rhDnExkAJbZMV1iR9VvZAD9NzMCLWj0zLsf5Px/5+cyShOtZD13TC8DcMha+7S1tg7gdgA3reUP8DsEcaCTZFTSu2cyYCdLIqKNgHfjiajIggDYMqkxUQkRGjePXqlOU6JyoBFqhWpk3DgX5Up5S6FO555mu+mutGNa7hkJWA6XP89vksq814pVLUQrW8/EdBeA5zOfH04e6yIit4jID0TkB0ePHh34xUuh7jrA7gNXlBmS3K8rGhHRRjTIrFMiomG2c7SEauwSxlKgk+kKkp7/9N16gc6sUKCzwxnoxd10e/nnjsTdiWlpgEZzq9kh7cWjYkQrW8/Mrd87cFFnIWvtp621B621B7ds2TLwi4+Wgq43eahdqUcUuJ1To4SlvES0afBmPBEVnVKSjo0pha4CLjI6TSZDo9KPjZZFDYVkFXOc+3XdXckgEx6I6LVbz668hwHsyXy+G8CRtXrxQKmuu2KBT0yTgGa0Yj0/EW0aTEyJaCMxyiWe2bOf2cTQKMVdSKINZj1v/TwI4FwR2S8iIYD3A/jKWr241rJosLHKzC01fYYjExFtVCzlJaKNRCfNLeNM+WyU+ZhVcUQbz7rtmFprmyJyK4B7AWgAt1lrH1+r1+9NPH3XNp+shpp30oho82C4I6KNREQQBwrNdicZzZbuso8I0caznqW8sNbeDeDu9Xhto7p3TOMkMfXJKpNSItpMBj1XRURUFK5nSCcBzV7bcceUaONZ18R0PZmeA+iVyP1ROCKGiDYjRj4i2oiWnkfKqEe00WyYOog4cONjuGtARJsRq0SIaCNaKgHl9R7RxrNhElNg8S4qERERERUXb7oRbR7M5IiIiIiIiChXTEyJiIiIiIgoV0xMiYiIiIiIKFdMTImIiIiIiChXTEyJiIiIiIgoV0xMiYiIiIiIKFdMTImIiIiIiChXTEyJiIiIiIgoV0xMiYiIiIiIKFdMTImIiIiIiChXTEyJiIiIiIgoV0xMiYiIiIiIKFdMTImIiIiIiChXTEyJiIiIiIgoV2KtzXsNKRE5CuDZVXzLFIBj67Sc9VbktQNcf56KvHZg9evfZ63dsl6LyQNjXaFw/fkq8voZ6xjrioTrz1eR179msW6oEtPVEpEfWGsP5r2O16LIawe4/jwVee1A8defhyL/zoq8doDrz1uR11/kteelyL+zIq8d4PrzVuT1r+XaWcpLREREREREuWJiSkRERERERLkqemL66bwX8DoUee0A15+nIq8dKP7681Dk31mR1w5w/Xkr8vqLvPa8FPl3VuS1A1x/3oq8/jVbe6HPmBIREREREVHxFX3HlIiIiIiIiApu6BNTEblORJ4UkUMi8uE+XxcR+ZPk64+IyCV5rHMpA6z/nyTrfkRE7heRi/NY51JWWn/meZeKSEtEbj6T61vOIGsXkXeJyMMi8riI/M2ZXuNyBvhvZ1RE7hKRHyXr//U81tmPiNwmIi+LyGNLfH2o37d5YKzLF2NdfhjrNhfGunwVOdYBxY53RY51wBmKd9baof0HgAbwEwBnAwgB/AjABT3PeS+ArwMQAJcD+F7e617l+q8AMJ58fH3R1p953rcA3A3g5rzXvYrf/RiAJwDsTT7fmve6V7n+fwXgPyUfbwFwAkCY99qT9VwJ4BIAjy3x9aF93w7x3/fQ/s4Y64Z77Yx167p+xrq1//se2t8ZY93wr39Y413RY12ypnWPd8O+Y3oZgEPW2qettXUAtwO4qec5NwH4nHUeADAmIjvO9EKXsOL6rbX3W2tfST59AMDuM7zG5Qzy+weA3wbwRQAvn8nFrWCQtX8AwJ3W2ucAwFpbtPVbADUREQBVuADWPLPL7M9a+1249SxlmN+3eWCsyxdjXX4Y6zYXxrp8FTnWAcWOd4WOdcCZiXfDnpjuAvB85vPDyWOrfU5eVru2D8LdaRgWK65fRHYBeB+AT53BdQ1ikN/9eQDGReQ7IvJDEfm1M7a6lQ2y/v8O4HwARwA8CuB3rbXtM7O8122Y37d5YKzLF2NdfhjrNhfGunwVOdYBxY53Gz3WAWvw3jVrupy1J30e620jPMhz8jLw2kTk3XAB7B+s64pWZ5D1fxzA71trW+4Gz9AYZO0GwNsA/ByAEoD/JyIPWGt/vN6LG8Ag638PgIcBXA3gHAB/LSL3WWtPrfPa1sIwv2/zwFiXL8a6/DDWbS6MdfkqcqwDih3vNnqsA9bgvTvsielhAHsyn++Gu4uw2ufkZaC1ichFAD4D4Hpr7fEztLZBDLL+gwBuT4LXFID3ikjTWvvlM7LCpQ36384xa+0MgBkR+S6AiwHkHbyAwdb/6wA+al1h/yER+SmANwH4/plZ4usyzO/bPDDW5YuxLj+MdZsLY12+ihzrgGLHu40e64C1eO+u9lDqmfwHLnF+GsB+dA4KX9jznBvQfdD2+3mve5Xr3wvgEIAr8l7va1l/z/M/iyE5JD/g7/58AN9MnlsG8BiAA3mvfRXr/1MA/y75eBuAFwBM5b32zPrOwtIH5If2fTvEf99D+ztjrBvutTPWrfufgbFubf++h/Z3xlg3/Osf1ni3EWJdsq51jXdDvWNqrW2KyK0A7oXrZnWbtfZxEfnN5OufgusY9l64IDALd7dhKAy4/n8DYBLAJ5O7U01r7cG81pw14PqH0iBrt9b+vYjcA+ARAG0An7HW9m2BfaYN+Lv/QwCfFZFH4YLA71trj+W26AwR+UsA7wIwJSKHAfxbAAEw/O/bPDDW5YuxLj+MdZsLY12+ihzrgGLHu6LHOuDMxDtJMlwiIiIiIiKiXAx7V14iIiIiIiLa4JiYEhERERERUa6YmBIREREREVGumJgSERERERFRrpiYEhERERERUa6YmBIREREREVGumJgSERERERFRrpiY0tAQkUtF5BERiUWkIiKPi8iBvNdFRLSWGOuIaDNgrKPVEmtt3msgSonIRwDEAEoADltr/yjnJRERrTnGOiLaDBjraDWYmNJQEZEQwIMA5gFcYa1t5bwkIqI1x1hHRJsBYx2tBkt5adhMAKgCqMHdYSMi2ogY64hoM2Cso4Fxx5SGioh8BcDtAPYD2GGtvTXnJRERrTnGOiLaDBjraDVM3gsg8kTk1wA0rbWfFxEN4H4Rudpa+62810ZEtFYY64hoM2Cso9XijikRERERERHlimdMiYiIiIiIKFdMTImIiIiIiChXTEyJiIiIiIgoV0xMiYiIiIiIKFdMTImIiIiIiChXTEyJiIiIiIgoV0xMiYiIiIiIKFdMTImIiIiIiChX/x+nw4r2zbchSgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "expected_te=np.array([exp_te(x_i) for x_i in X_test])\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(X_test[:, 0], te_pred, label='LinearDML', alpha=.6)\n",
    "plt.fill_between(X_test[:, 0], lb, ub, alpha=.4)\n",
    "plt.plot(X_test[:, 0], expected_te, 'b--', label='True effect')\n",
    "plt.ylabel('Treatment Effect')\n",
    "plt.xlabel('x')\n",
    "plt.legend()\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(X_test[:, 0], te_pred2, label='SparseLinearDML', alpha=.6)\n",
    "plt.fill_between(X_test[:, 0], lb2, ub2, alpha=.4)\n",
    "plt.plot(X_test[:, 0], expected_te, 'b--', label='True effect')\n",
    "plt.ylabel('Treatment Effect')\n",
    "plt.xlabel('x')\n",
    "plt.legend()\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(X_test[:, 0], te_pred3, label='ForestDML', alpha=.6)\n",
    "plt.fill_between(X_test[:, 0], lb3, ub3, alpha=.4)\n",
    "plt.plot(X_test[:, 0], expected_te, 'b--', label='True effect')\n",
    "plt.ylabel('Treatment Effect')\n",
    "plt.xlabel('x')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Interpretability with SHAP Values\n",
    "\n",
    "Explain the heterogeneity model for the constant marginal effect of the treatment using <a href=\"https://shap.readthedocs.io/en/latest/\">SHAP values</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x223.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values = est.shap_values(X)\n",
    "shap.plots.beeswarm(shap_values['Y0']['T0_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values2 = est2.shap_values(X)\n",
    "shap.plots.beeswarm(shap_values2['Y0']['T0_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|===================| 983/1000 [00:54<00:00]        "
     ]
    },
    {
     "data": {
      "image/png": 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I/t9m91uQ8905UXphh+8gANe1+UylaX/AyG4r+scuK4K6ToLj0HKIu7b8uVwHPnMyzH8bFq6yJyG5PnQYfPxo+MSt26e5I5NHwuKf2pOiXdMrgbFaLu/0WD/UXD8wAjG01oC/BhwKPAE86vv+Nz3PKwa2AKf5vj8vWvckYC4w2Pf9Tn5Ze9QuBeIp/5vh3RqyLXXbp2gMZB/sIDlvNNkaJi1tTcC2nTj6Z0sHkGkL0HFj2DeTIZ49rJi2w5wT1YBLgpAiY4iH7YcjxIMAxxiGNDYTC4PWQFjW0oIRobGore8xlk4zqK6+XR+u25Ky/X7G4IQhbjrAxOzreHOqXe3ROG5reWKptA2sga2JimkLZk4Y4gRRMIa2AJltrjR2JLeTU3sPHCGes08wBrdDrIk32abz1t3vCqErGLEnAYmmsHWfCRCKgxsG7faXGx0YY1Fwyx5a2x9OTOshM6t9IM4Grrb12+ZDYlENMfuOYANx7jbS2EF7Trsfl7FN1a0lzOYle6C36cfJIDnDUnKDW1t+bfDLzVvHmnM2bGbTdWz9LHo/wCHMSdWmFiONwc2pKYNLKgrd2ZARtp5QtJUuxI3yQ872M+R2x4TESLVLKduWIe0CGu1Szn1t6PjdtF+j/Wei+WQMggySzrk/bevvwwZVIfe+ALnB2Da9m9ZtB9hhNdl10znfi3RII3tK1jGX27dC9aoTZ8KTC3r+ucvOgBs+tatb1UDcQz055fkxsBB4Hdv2cmW0fBqQBP6Ts+7LQDEwdQ/kcYfq6up2eR7o/CdjcpqgIfqfMrbUrrQ/8xRBilzbxFWSgNKidv9baZF2/66t6UVBGBGaXIe0QIbWeEZoDBkR0o7DppIkAVFANIba4mK2lZTYJucwJJ5Ok8hkCKKga4tg2gbfiBCKDWoAsZZ0Ww0WkKB9eYKYSyjYEwvJ2UnG2CAMOCLtm786zLc7JBpj91uHXdD61xjSRQ6hE712wDg2GOMIJu7SUuJgbOQnlNxwlj34h1HQzQ2o3dG+BtzZgTHbPuBgiEWhJJsDweSEZltHKiIg3hrswtYaY+7Jgc2fPSlwolqm29qk6bSGtdxSdCzP9q/b9kj7to62Wp1BcKJAlHslptsuvdzwnEGivRqS7ZlvC+NOdNKQLVfn33LuiUTut9PxFKh9vrdPpeO6bcJRlZ2sjW25GtSh33e7ml5XoT03L9lvvZNg3y5fAfY/Odg+L51soddN3bWBdMFDL7bO79JxtReErb+99lN/0O1S+L5vgKeBYcA9vu9n24HKo781Oatn5yt2N4M7U15evkvzd77fIZFb+mg08nbNO9n3wP7fJRxI5NxyUsCU5txBK9Y+3ZLQ4IpQlIz+UUUodg0lQdDW7AukHIe041CWSlHekmJQKk1JOkNJOtN6CHCATCxG4LogQiqRIJlKkcxkcIFMPLfnlJyDuZ2xA7VCnGyTelRmJxO0ldEYMGFr7RSR1nXimbaDbpf7CNuYl3sIO/iag5j2lRkYiTYrQihQechgxLEZldD2DePaAG86jDiNpUBC+zmiE5JYVBOLRYO2nI4nPNAaDE1rwN2+Ntx2eG07+LbOSTZktTWVtuYpCk0GhwwuaRwEohCc/W5Nzt7osMta082+yn4iG6S6CsIGk4ghVcl2y3LZsJmJmpHbB7quTlBC3GirGWI0E6M555RAoj1gcKNTj9zmeYNDOKwCKnODXtsvMVv/bZfPoeVQnsS5bA6cfXjrPm+boj2XiMGJ0+3J7qxxcMNH4cJjYdpIu+zKD+Iu+K7932yXQggfmIU89jWoLLZplRch91wEl5yKzJkF00d12HMGzj2a9k3M2e+i7f/euA4Ux6M8Zhvv7W8t91to3ffjh8KFJ7W7PEjOPc6WLevLH2h/Qjuo1E5jhsBBk2zz9JAyOP6AtnVcB6aNtn2+75kGwyvb3hs7BG45H8b1/I577vEzW+d7eoztPdLF1Pf1pGl6JvA88BPgc8As3/dXep53EPAKUOX7/rZo3Spsc/WBvu+/3gv5zrXLJ5apwHDuIyF/WhQF4Xan3qZtgFbHLbgCLVETdcy1fVVZoWH6xloaRUgD5QaaBO6/tJJN1SHDKh1m75tgwaJmvvTDraQNJEzbwbEknWF4S6r1QJQNhMl0hrJUmrTrEuac0Q+qr8dtrUaHFLWkQMQ2Hede3pTJEMvY17FUunWgliFq7k2HGEeY/tHxLHtyHc1b2pquYxhI28FaIlA0KIGbCknXpkkOS3LAxfvhxB0W/2YJRYOLSG1oomZRDeIIs2+YzT7n7gNA9UvV1L1j+wMqpg1iiDeU+rdrePVz/6Z+aR0pN41Tne1bhiBmg7JkQuKNIa6xA8xaB54RtvbN5jYLlh9QiZt0KZlRRePrm2leXkfRpAoGfWA8jf9aB6Gh/MQxVJ40hsyWFtZ95yXc0jgV7x9LzUPLIe5QceoEyg4fwcrPP0PqnW3ZL5ciMq214WwgCqMQ3RZ+W39EhJCzfog7tJj4qBLSCzdDYHCHJnGKHWRTva29RetK3EXStt/VGV9JYmIl7pTBJM85kHBDHYn374tUFZP+2yJMOqD5b++Qvsu3J1xRLdwAiTNn4kwdSvONz0YnmSExsoPB2oJI63arksRKHdzVmyARIxxSCutqCaLbzWSbpTue1MiHDyT26/Ogvpnw4dcJHnqTcOU2eGsNhAYZVUnsr5+Ff7xF8NvncbwJuHd8CilpO4k1zWnCB17BPL8UnlsKFcW4N5yJeBPpDlNdh/n7QmTfEXbAU2MKPnRwu9HjnX6uvhkuuhPeXI2ceiB877/gnmfhpocxk4bBmYfByEHw0jJ4+BUYPwS+fxYydjDcMhfu+w8s3wg19rfdxgHiNkguuRVGVEJTC8z9jz0JOXEmrNkMz7wJsybAARPs6x88BBOHw8Vz6PISqFXV8OxCG6BnjGv/3qMvQ2MLfPBQiMfspUqPvmyD9KZa2FwH//cv+OfC7dPdZyQcvz/c8fmut71zvRId18vVnR7rR5rv9Plo3N3BWkXAS8Bc3/cv9zzv18Ak4ESgCBt0P+D7/pPR+icCD1PAfcS53toccv1zhr8sCmnJHUQkAiaE5pyzXMH2B9dnouqUtN5py8kEVNU0UZwJKTeGAKgWod4RHv1CJSdOjbfbbvWWDN+4rZoVG3PSNzC4JUV5JtPalAz2hh3F6ajmGv2DuEFASUsL8SjgxjAU1TUSxFzcsG2UM4CEgb0LF+CYEDcqqPeFqex/xjjCtL00pWx0CZsWbOVvn/4XqYYMs86dwkEXTeWNu5eQqs8w9SMTGLr/IILmgPqVDZSOKyHW4aYVJjTULa0jObSIRFX3r6GdO3cuZkmGEU9XsOUf6zAtIYjBCe2OjzkGJ9O2r1zHljG3CXfQe0cz64k9d8lI3dNrWH72EwT1aUoPGw5hQOOzaynKpHJ6dg0dGy3B/jATM4eSfmMjTnGM8v+eybDbTkJch2BbM8H6BuL7VCExB5MOqP3KYwRbmqi4+jgYXEzd5U/iVCQpv+pYnMokOxNuaiDcUEe4qobUI2/jjhtE8itHIYkYpilN80/+TdNVj+NkAtxZw3GCkPCtjcg+w4h9+jBip0zDnTQYYg5maTUyvgopS5L5zb9h/hLkpOkEj75J8LsX7ZnSlKHET56K7DcS5+ITOr0kyWyohdomGxz7s3QGlqyHH/0N7n7GNgkfuz+EAp85AQ6amO8ctheGMOic9oPDjpkBz1y3OwE4q1cC4zq5ptNj/ShzzYAJxLcCxwJH+r6f9jyvHHgN+Lnv+7dEo6anAx+JPnI/8Ibv+5/rpXzn2mNdLYfck+GVDR2XGtvalB1eHRdIGXurS7CBOObg1jYzuqXtspMaR2h2wQRw1OQYj36+gqJObjm58J1mvnzLZkKRqDYDJZkMQ1pSdtvRP8XwQULDhhRiQhLpTNuIaWNIpu1gIe/kwewzLYkbEx757ltI2gbeorIYc66aTtPWFBMOG4wbd1g+fxODxpUw6qCqPbX7dtvcuXMBOP300wnTIRvuX0GYCck4UDq+jKqDh7Dw3H9S9+oWRp67D6PPmczSi58jU5ui6tRxJCeXM/TMSTh74S5im777AnW/fRNxBScZIzFzKLHRpWy78w3Y1AgCVZcdztDvHdPreVFqlzy3CC77LTS0wCeOhs+fAsU9v/lMJ3olMK6Vazs91o82V/X/QByNgH4QmO37/qKc5UcDjwNHAEux1xFnA3GfuI64o8E/zbC1Q47HlsGWFmhMGzsauiVsP5DLsQOKiuqbGdHSNjIzFRP+c/UgqusN00e6xN3Ofytr16f5n2+tpT4eb+1dqkqlqchkILqeOCyL88NbxvHsY1tpaghY4tewYUPbtorStqn5g58dy5GnDQNg2UtbWPTkJsqHJTj04+NIlBT+TdRyA3FflnprM1IaJz6+14dIKFWIeiUwrukiEI/pB4F4p0fn6JKksk6WzwdKcxZ9Opr6rK95DpfPtwOZKhIwfQg8dIZDTAxDfmLsaMtisXfSyt5lKx1COqClJEEqFZAwtl9w8FCX0ZUuoyt3uEliUT9oAK2135p4nPKM7YsLRHjvyZVUVsY47SwbZN+ZXcovrl1hR8CGbQNhRk9pGwgy6dDBTDq0d+9IpDqXmK6Po1RqT9PnEQ8Q3z7C4SP7CoGB/Ye2nWQ9/m6HkZCOAy0tHcZlCOuL4yQch4zjsL6hwx0rujCkymWfcXH+s64tscARe/kRhnTMpTnV/kRw6oHlXHrzZH5+2bukA3uCMGFqMROmb3e+pJRS/UJ/eeRhZzQQd7DfkO2/7AkV0O4qyexo6eyo5GQ8urQpTgrAGEJnxyM1s1xXuPabI7ju9s08v8g2N5dn0jhif3gZx2G/qdsP1Bk7uYRv3TGNBf+uYdDQBDMO02ZQpVT/1fmtXPoHDcTdUJGg7Zp+Y6AxbS8FEYGES9KEfOu4Iq57KkPGsde2DTHdqxEDlJY4fP+SYaxYn6GpOWTiyBiPPFbDstVpZu1fzFGHl3b6ufJBcd4zZ+dPUVJKqb5Oa8QD3FOrabtWOB3aQVsAGN4/0eXhT5cScx3eOwb+5/+acEX45cd3frlJRxNGtn0dH/lw4YxmVkqpfNNAPMCV5dw4i0zY7r3JVQ6x6PrJI6cmePWKBEoppfas/hyI++8wtD3og1OES2YLkyrhQzNiDCm1P4iSOJwzWwOvUkr1tkK917SIvFdE7hSRudFrT0RO7EkaWiPuBhHhBye4/OAE+3rbnApeXBkwbZjDhMHdG5SllFJq1+2Vh2T0kIhcDHwZ+BXw0WhxE/YhSe/pbjoaiHfBoGKH903L/5mYUkoNFAV6HfFXgJOMMctF5JvRsrexTyXsNg3ESimlCl6BXr5UDqyK5rOV9jiQ6nz1zhXkKYZSSimVK/u8r45Tnv0TuKzDsi8BT/UkEa0RK6WUKngFWiO+GJgrIp8BykVkEVAL9OiG+RqIlVJKFbwCqP1uxxizTkQOBQ4DxmObqV80xoQ7/mR7GoiVUkoVvAKtEWPsIwxfiKZdooFY5dX6esPHftfEsjrhfSUTOGP8inxnSSlVgAqxRiwiq+jiyipjzPjupqOBWOXVmd/fyPHPLOb9QcCTk0Zx27JZPCgpvntCnJGlhfePp5TKjwKtEZ/T4fUo7HXFf+xJIhqIVd4YY3jfU2+TjDvgOhy7oZrHy8dx59sO7767hSe/pM/1VUpZhVgjNsY803GZiDwNPAb8qLvp6OVLKm9EhEwyTtp1aYnFaIm5VFfEeeqHN7NkbQYT9Gi8g1KqHzM4nU4FqAWY1JMPaI1Y5c3Pf7WewBGc0OAYQ3k6IAwNB65Zy5s3XUPdX8dSsejyfGdTKVUACvQWl9d2WFQCzAEe7Uk6BXk6oQaG5x7fiBuYdv9gh69YSa1TxlJ3InVLagjvfT5v+VNKFY4Q6XTKs3EdpiRwK3BeTxLRGrHKm6Y01FWWU+u6FDc389LowRy7YAFvxmcAsNYdTeKeNxj2iSPynFOlVL4VQNDdjjHmgj2RjgZilRdLNmQYEou11oabkklKmtMc/dp6Wki2rrd1UR3D8pNFpVQBKZRA3N1HHBpjnuxumhqI1V43f1GKC39YzQdTKVoS9nnOIbC2vISypjSV0kKDU0QFm5i07HXM4d9E7voi7D8uvxlXSuVNAY2avrMb6xhgcncT1ECsetVfFoX8Y0XISROEj02zz26+6ddbmNDYQkVdPYtHDkMQXhg+iBNfXUQJGTBQFDQyhUXEScGLizEHXAp3fR654Pi2xIMAXH0etFIDQaHUiI0xPRoR3R0aiFWvuef1NOfdlwLX5ZfPGUZWCB9p2obfEqOyopQ/zdiXZtdhyaBSEOHjdY05nxYyJFpfhTjUfPoBam5fQ8V+JZQ//BjxLdWYRIzMlz9G/MYzESmMf1Sl1J5XQDXiPU7sbTK75nleAngRmOf7/qU5y78MXALMAm4D3gtUAg3YoduX+r6/tZfynasQR7UPeOtrA0b9oAWKYpANkA0tDE+lcFyXoXUtresuL0tSXxTng6+9xXcffBYQYqSZzGsUUY9BWMtUNjEKAwxlHWNYgkMAQIhLmjISRWn445eRDx+69wuslMrqlYj5tNzV6bH+ePPpvEVoEakArgGOA4aSU/ae3OJyp5cv+b6fAj4JfM7zvBMBPM87APgecK7v+zXY4dr7+b5fAUzHXkv1s+5mQvU/k3/UDMa0BWGA0gQlrmA61FxNaCAdsKk0SREtFNGMEFBPFWmKaaaYRoopIkMRGTYzEomCMIAQUEQN0tIIZ9yA2Va/t4qplNpLCvR5xD8HDgGuBQZjH4u4Els57bZuXUfs+/6bwLeBuz3PGwn8Afip7/vPRO+/4ft+Q85HQmBaTzKi+pemNJAObTDOcoRkJqS6Mkl9MkbGEYSQhpjt5z1s1RrSJEiTICCBSwtxmkjSSAyXOAEJApJk2jVb5/4rCsD8t/dGEZVSe1GBXkf8PuBMY8yDQBD9PQv4VE8S6ckNPX4MLAReBwLgytw3Pc+7zPO8OmAr8GHg+p5kZFfV1dXpfAHOi2tsp0EqbX9lLuDA1OpajllZTaUJ2bexiaJUhjE1dZz07moOqa4lFWv7SSaxfcYGlyAn8LoE1DGazhhApo8tmP2g8zo/0OZ7S4HWiB2gJpqvF5FBwDpgn54kstM+4lye530L2yR9ie/7nVa9Pc+bBHwa+LPv+6/3JDO7SPuIC9ALqzMc8Zs01LRAcQxiLuO2NXD4xlrEFRxgWyzGk1UVpF0HjGGoyXDpM89z5ktv4BIykmUUU41gWMt+NFKFwVBEC0maqGQVRTRgsPehpSSB3H4R8qnj81x6pQa0XomOj7v3dHqsf39wbj77iOcB3zPGzBORe7GtwfXAbGOM1910ul0j9jxvJnAFcCNwted5nXZE+76/DJgLPOJ5nt5Cc4A6fGyMW94bh8okOA4kHGLjSxkywuXl8hLeKU7y5OAoCAOIUJ0o4to5x1Ei9RTTSIYS6hnFJvYhpIhyNlA8OcngfVIkiptoKqqkedJk5NErccxfcBr+oEFYqX7KSOdTnn0GWB7NfwloAgYB5/YkkW5dvuR5XhHwe+CHvu9f7nneCOAez/NO9H2/s0fkxIAxQCnQ+20WqiBd7Lk8tdLwxHKHg4YJT52doDQxgWmXbuSVZAmVqRQ1ySKyJ9DHrV7Pa+OHU+zWUZRJU0clZdRTGvUTbz32SMY/88n8FkoplRdhLP9RtxMrjDEBgDFmE3DhriTS3euIbwBS2GHaYCP/a8AlnufdA5wCPOT7/jbP86YCNwHzfd/XIDyAJVzh4Y/Ft1t+zmFJrnshIB13mbNmA3WOy9DmFrzNW3nviqWUZJoBqGITaxlO0ajBuB+eydgfduvOckqpfsi4BRmI14vIn4E/GGPm72oiOw3EnuedBFwEzPZ9Pw3g+36d53nnAo8DLwDnAz+Mas7V2OuIr97VTKn+7dsfK2dkopY7H6tjWkMzQ2vbztfes2JpTgeTS3x0BaPXXJKPbCqlCkhQmDXi9wGfAP4gIiFwLzYoL+hJIjsNxL7vzwPKOlk+H9v0DKBVFdVtriN85oxK/u/BLbQk4tQWJylpSVFTlGBE7YZ26wZjhuQpl0qpQmKcwgvExphXgFeAb4jIcdigPE9E1htjZnU3HR1MpfJmUWUxGxJx6kpLWDq0ipfGjOSKUz/O2vIqDLCRKlxvYr6zqZQqAKErnU4FZBHwFrAKmNiTD+q9plXefPjwEn76in3k4cyNmzljwTuIMawMJ7KmaDSVQZrpVxyW51wqpQpBUFhBF4DouuEzgbOBI4AnsFcWPdSTdDQQq7z58dkVrH9oIQeu3kRRJqDRwKjqrTQ7MUrjzUx/+Wxio8vznU2lVAEogEuVOrMW+Df2bpMfMcbU7GT9TmkgVnl1xLtrKUqlAYi7DvMOmsaZ8xew/wVTiM0YkefcKaUKRVCAfcTAFGPMut1NRAOxyquEaXt4QywIOW3DOxx/9X4M+/zBecyVUqrQFOhgrd0OwqCDtVSeHXpR2y1ZkwcbKr8Z1yCslNpO6EinU3+gNWKVV4d/ZgrT3jeSTEvAc4ueyXd2lFIFKuwfMbdTGohV3g2aEF2Ovii/+VBKFa7+UvvtjDZNK6WUKnihSKdTPon1GRF5UkRej5YdKyL/1ZN0NBArpZQqeAXaR3wt8N/AHUD2iYSrgW/2JBFtmlZKKVXwTJ5rv104HzjYGFMtIr+Ili0DJvckEQ3ESimlCl6B3c4yywXqo3kT/S3LWdYt2jStCocxSLqzx1srpQY6I9LplGePAreKSBHYPmPgOmBuTxLRGrEqCJnX1nHIp14mUZem7gsO5T85Pd9ZUkoVEOMWZL3xq8DdQA0Qx9aEnwDO7UkiGohVQVj6sQfZkBpFOuGS+d0mDpqzjBGnTsp3tpRSBaLQ7qwlIi7wUeyjDyuACcAqY8z6nqalgVjlXZgOWbNGeHn6PjSXF9FQVsIb31vBJadMRPLf9KSUKgCh4+Y7C+0YYwIRudUYcxfQDGzc1bQKsq6vBpaGhZupLUsybOtWxq1cz6hVG9hYWU5jTSbfWVNKFQjjSKdTns0Vkd3uR9Mascq7hRc+RV1RCZsGlfPgQVN5bvxwRja20PSDdfz4uvE7T0Ap1e8VQNDtTBL4i4g8B6yibeQ0xphu9xNrIFZ5t2FFmupxw5i8bQufe+EVitzZzBs/gmc3pbjw4RS/Oi2R7ywqpfKsAEZId+aNaNotGohV3lUPKufkN9+krKUFgLEbq2k+4/2UpDM89EIxaCBWasALC3DUtDHmO3siHQ3EKq/qvvMUEqM1CAM0xGNsKS9lMzCqtpkwqMApzIv5lVJ7SSHWiEXkxK7eM8Y82d10NBCr/Fi8jpbvPMKGe5cy0xnKlpJSBjc2APDFj5/GsqpyAKqaUzy5oIWTD0rmM7dKqTwLncKrEQN3dng9DEhg7zfd7dtcaiBWe5255W+EX/8jQpJxwN8mz6JC6hhTH7KlbBirK8tb191WFOf8PzayWgOxUgNaIdaIjTHtbnYQXVt8BVDXk3R2Gog9z0sALwLzfN+/NGf5l4FLgFnATcCJwEhgK/An4Erf95t7khnVzzW1YK78E+EP/kYLwwCHNDHOWfIiZTTgYGh24hy1ZDZPT5sIQDwIWJ8RXl2T5qAx8bxmXymVP4XYR9xRdG3x9dga8a3d/dxOS+b7fgr4JPA5z/NOBPA87wDge9jbeGWAauB0YBBwDDYo39izIqh+7dV3MRXnYH7wIEJAnFoaKGIZE1nJZJYxmRAhGab5ypP/5pP+27xnTTXvWbuVaS0ZjrytjjA0O9+OUqpfKtB7TXfmvUCPbprfraZp3/ff9Dzv28DdnucdCvwB+Knv+89Eq1yes/oKz/PuAi7qSUZUP1bbiDnmCiQTAA5CiEM9tUzGROeCTZSyza3AHz+B+WOnUFxSzP41DRjsA8HXDKrkN6+k+fRsHUGt1EBUiH3EItLu2mGgBHtt8Rd6kk5PSvZjYCHwOhAAV+5g3ZOi9XpdXV2dzhf4fMPbK5D6Zgwu0HYGm6Shdd4Al3zoHE6/6CJu+cCR/HPcEIjWLs4EtCRdXt7YdqetQiiXzuu8zm8/31tCx+l0yrNzgE/lTKcAo40xd/ckETGm+819nud9C9skfYnv+7d1sc5XgG8Dnu/7K3uSmV2k7ZWFLpXGHHwpLFyHIYaDvVQpg8ubHElAghUjB/OFC05lXWVZ68c+tWAFsdDw94nDWFtezIsXxJk9Mu//eEqpHeuV9uLvvvf5To/1V/z9iLy1T4vI14wxt3Sy/BJjzJ7rI87yPG8mdjTYjcDVnudtd+9Bz/O+ClwGnLiXgrDqCxJx5PkbMdd/nFBcQooJSVLPcAwOLiGHbljMjHXrWj+STGdYnizin6MGs60ozpEVaQ3CSg1goSOdTnl2VRfLr+hJIt2qEXueVwS8BMz1ff9yz/N+DUzCBtwwWudK4LPASb7vL+pJJnaT1oj7EmNomnQZ7orNGAzrGEdAEaU0U11awndPPYl3hw3j6HfXs7m4mEUjhlLR1MTjv5msT2JSqm/olX/U75zyYqfH+qsfO2yvHxhybuQxFziN9mWeDFxpjJnQ3fS6ex3xDUAKuCZ6/SXgNezlS7d4nncz8F/Acb7vL+3uxtUAJELx8hsJf/0UmWvnMmT5RhqoAmIMbWjktr/M5Yn9D2JLeQXlqQyp8lJmTHI1CCs1wAVSUC1i2Rt5JIG7cpYbYD1wcU8S22mN2PO8k4AHgdm5NV3P844GHgfmAE9jA3U656MrfN/fvyeZ2UVaI+6jTCZg2yG3smBJMZXNKUpMiq1SxrKhI1g7ZTCrBleyafAg/vfOySQTBfVPqJTqWq+cNV855z+dHuuve2R2PvuI7+nJU5a6stMase/784CyTpbPB0qz+dndjKiBR2IuFU9/kfvOWsCcJ99u/RWV1KWoL07ijxhGaUtKg7BSqhBGSG9nTwRh0FtcqjxzBxezeMxg4rEU6Yy9Rnjz8ApSySQtwFeSW/ObQaVUQTAFWN0TkQpsl+1xwFByKqXGmG4/TF0Dscq7U95ezmuzJlBam6a5uIhNoyoJgcEmwxl3Hprv7CmlCkBQgDVi4OfAWOBa4HfY64q/DtzXk0QKsmRqYDnlmFJEoKkiRl1VEicIWJ2Mc+1XR+ggLaUUULC3uHwfcKYx5kEgiP6ehb25R7dpIFZ5N+UbM5m8eD2xTEBFXT37LlvDCS8twTugdOcfVkoNCIEjnU555gA10Xy9iAwC1gH79CQRbZpWeecOK2Nk0xZGLaimrqiUJreIA646GMn/P5lSqkCYwhwT/Bq2f3ge8CzwM6AeeKcniWiNWBWEigNHUxtUETYWMaghxdj/nprvLCmlCkjgOJ1OefYZYHk0/yWgCfsUwh6NptYasSoIU/5yCrWn/QFnW8i0H78PtyqZ7ywppQpIAfQHb8cY827O/Cbgwl1JRwOxKgjxceVs+O5oALzTp+Q5N0qpQlMA/cHbETua9ELgE8BQY8wsETkWGGmM+b/uppP3er1SSim1MyHS6ZRn1wL/DdwBZK8bXg18syeJaI1YKaVUwSvEGjFwPnCwMaZaRH4RLVuGffBDt2kgVkopVfDCAuwjBlzsKGloe+5BWc6ybtGmaaWUUgWvQK8jfgS4VUSKoLXP+Drs4xG7TQOxUkqpgmeQTqc8uwQYjb2pRyW2JjwB7SNWfdE7f1pOw80p4lOB0/OdG6VUocnk/5rhViIy0hiz3hhTC3xYRIZjA/AqY8z6nqZXOCVTA9aCn7/FY99dxEtVk1j+RhX/OOqRfGdJKVVgQul8ypOOd8663Rjz0q4EYdAasSoAL//qLf41ezYAq8cMoXT+m5yc5zwppQpLIAVVb+x4CnD87iRWUCVTA9PrIya0e33/kfuRrknlKTdKqUJUYDVis/NVuk9rxCqvVq1PMbipiY3pDKl4DDcIWFccZ8MbNYw9ali+s6eUKhCZwqoRx0TkBNpqxh1fY4x5stuJ7eHMKdUjv36+mca4yyeff5KRddU4JsN5Z59L7aYGQAOxUsoK8j5Aup2NwF05rzd3eG3owU09NBCrvKlpCGi54UXGNtRQVNdCeX0To5u28Ngvf8KyZUfDhyfmO4tKqQKRyf81w62MMRP3ZHoFVddXA8u2VY0MrmuipAmWlY/inyNnsjVRSkVLM/EnluQ7e0qpApIWp9OpP+gfpVB9UrFr2FZZ1vraiENtvBSDYeGokQTravOYO6VUISmwwVp7lAZilRfGGD71/Q28MWYE6ajJKeMYYjQSJ83Hlj/L8gk3Ub2hKc85VUoVgpRIp1N/oH3EKi/O++56ViTKqB3uwNSJ1LsxYrRwwZInAHAxlMZqkNFfpOmRr1D8/pl5zrFSKp8K9KEPe8ROA7HneQngRWCe7/uX5iz/MvY+m7OA84BPAjOBtb7v79M72VX9wZMvNPDUeocDtmzhI6+8Q2VdMwbYr3oNhrbx//WxMoY1GTjlNoLRZTg3fxw5+z15zLlSKl9a+nEg3mnTtO/7KWyQ/ZzneScCeJ53APA94Fzf92uAtcBNwPW9mFfVT1x4bxNNjsMB6zaxePAgbjzlSH5x/CEsGj6CzYwgTZxmkmxzKhESpKhC1q6DT/4I85378519pVQeBCKdTv1Bt/qIfd9/E/g2cLfneSOBPwA/9X3/mej9v/i+fx+wptdyqvqFF15vYmNRnIQxbC5O8mdvOtXlJSwbVsWdR83GAA1U0EQpM2sWU8wWXAIylCCE8CO9D7VSA1GzSKdTf9CTwVo/BhYCrwMBcGWv5KiH6urqdL4PzW/Z1ki6OMaITMiz+0wgzHmiSpI0g9hGKQ2U0YDg4hKQYAsBcbtSUbxgyqLzOq/z28/3lrR0PvUHYkz3b5nped63sE3Sl/i+f1sn758PXLGX+4j36D0/Ve9Kpw2zrtpGKmXIpAMmNTSxrLSEeBhy/psLuOTpB1vXdUiRoJ6QAMHgEMI/r0WO2S+PJVBK7USvhMfRX6ru9Fi/9sdD+3w47naN2PO8mcAVwI3A1Z7nje+1XKl+Kx4XXr6mkvpUmjAImVHXwJz11ZyyYTPNJRXUJEujNQ0uzQhNhMSQ2y+EzB81CCs1QDWKdDr1B90KxJ7nFQG/B37o+/5lwF+BezzP0+uQVY8VFzlc/oFS6kVYn4xT3tLMmOrNfPIfz9DYUsbLFVN5U/ZhcWwCjVJB/J2bcT57MuLqz02pgapGpNOpP+judcQ3ACngmuj1l4DXsJcv3eJ5XixKKw6I53lJAN/3m/doblW/cdGJpfz+kXpWJxL86pHfUpMZSpoEAUnKmwI2jYL7TjqZ7180DPYdme/sKqXyrX/E3E7ttI/Y87yTgAeB2b7vL8pZfjTwOHAEcCZwdcfP+r6/N3ad9hH3Ua+uTHHqTduYs24jlzz4d+JBCNgv1MQb2afhcmJxrQUr1cf0ynFfLtna6bHe3FrV50P0TmvEvu/PA8o6WT4fyHboLaCttqxUtwyXgGNrG5i+fB1ryysYv20bAjwxZQKJZMh+GoSVUln9pBm6M3qLS5U3Q4YliBtDLBOwvqKCLSUlCDB//7Ec9fEJ+c6eUqqQ9ONArFUOlTdFSZeTji9l6fhRpF2XVCzGwwfvS1NJnLNKe/+6RKVUHyJdTP2A1ohVXh19ymBe/d07fPPsE2hOJMi4wvnzfYaMHZbvrCmlCkk/rhFrIFZ5NXmEC/GQL8x/ja0lMUbWNdEoCWKH6GXqSqkc/TcOayBW+eXGHCZl6khuDBmcLMLNZCAW5jtbSqlC049rxNpHrPLu1NuPp6iuhUR9I7HGFJPjGoiVUh304z5iDcQq76ZNLePAh0+lfnIJqYOSnPnUyfnOklKq0Ih0PvUD2jStCsIhB5ax5gspAGIl8TznRilVcPpJ0O2MBmKllFKFr//GYQ3ESiml+oL+G4k1ECullCp8/XhEkwZipZRSha8f9xH343MMpZRSqvBpjVgppVThc7RGrJRSSqleoDVipZRSha8f9xFrIFZKKVX4+m8c1kCslFKqD+jHgVj7iJVSSqk80hqxUkqpwqejppVSSinVG7RGrJRSqvDpqGmllFIqj/pvHNZArJRSqg/ox4FY+4iVUkr1KyKyXEQOyHc+uktrxEoppQqf1oiVUkqpPBLpfOr2x+VcEVkgIq+LyF9FZHi0/DkROTSa/7mIvBnNx0SkWkRKe6U8Ofp8jVhEHgeGdnf9WCw2NJPJVPdilvYqLU9h0/IUNi1Pr3jMGHPKnk7UfD22y3XiqJn6+8BsY8w6EbkO+AlwFjAPOAl4CTgaaBKRUcBE4C1jTMPu5n1n+nwg7ukX7nme7/u+11v52du0PIVNy1PYtDwDxgnAI8aYddHrXwKvRfNPAt8Wkd8Dm4FnsIF5EjZI9zptmlZKKdXfCWA6LMu+/hdwCPABbODN1pBPwgbpXqeBWCmlVH83D5gjIiOj158B/gFgjGkBXgYui5Y9DxwFzIrme12fb5reBXfkOwN7mJansGl5CpuWp//6h4hkcl5/G/i7iBjgXeCzOe/NAw4FfGNMRkSWAMuMMam9kVExpmNtXSmllFJ7izZNK6WUUnmkgVgppZTKo4HYR4zneT/DjohrAeqBL/u+7+c3V7vO87xzgG8AM4Cv+L7/0zxnqcc8z5sK3A0MwV5CcK7v+4vzm6td53neLcCZ2GsRZ/q+/0Z+c7TrPM8bAvwWmIL9n1kCfNb3/U15zdhu8jzvAewlKiH2OHCx7/uv5jNPu8vzvKuBa+jjv7mBZqDWiB/F/lAPBG4A/pTn/OyuV4GPA3/Icz52x+3Az3zfnwr8DHudX1/2AHAssCLP+dgTDHCT7/vTfN+fBSzF3hyhrzvP9/0Dfd8/GLgFuCvfGdodnucdAhwBrMx3XlTPDMhA7Pv+w77vp6OXzwFjPc/rs/vC9/03fN9fiD2z73M8zxuOvY7v3mjRvcAhnucNy1+udo/v+/N931+V73zsCb7vb/F9/+mcRc8DE/KUnT3G9/2anJeV9NH/HwDP84qwJ7CfZ/vrZVWB67PBZw/6IvA33/f77D9hPzAOWOP7fgAQ/V0bLVcFJDph/R/goXznZU/wPO9XnuetBK4Hzst3fnbDtcDvfN9flu+MqJ7rl33Enue9DIzv4u0R2QO+53kfB87GNiEWrO6WR6m94CfY/tQ+Nw6hM77vXwjged6ngJuBOfnNUc95nnck9hrYy/KdF7Vr+mUg9n3/kJ2t43neGdiz4JN839/Q+7nadd0pTx+3ChjjeZ7r+37geZ4LjI6WqwIRDUDbFzi9v7Ug+b7/W8/z7vA8b4jv+5vznZ8eOg7YD1jmeR7AWOBxz/Mu8H3/ibzmTHVLvwzEO+N53mnArcB7fd9fnufsDHi+72/0PO9V4BPA76K/r/T1Ubn9ied51wOzgQ/4vt+S7/zsLs/zyoCqbD++53mnA1uiqU/xff/75Aye8zxvOXCajpruOwbknbU8z9sEpIDcA/1JffBMGADP8z6BbVarwparAXhfNICrT/A8bz/s5UtVwFbs5UuL8purXed53o+BjwAjgWpgs+/7++c3V7vG87z9gTeAd4CmaPEy3/fPyF+udo/neSOAB4FSIMAG4K/5vv9yXjO2B2gg7nsGZCBWSimlCoWOmlZKKaXySAOxUkoplUcaiJVSSqk80kCslFJK5ZEGYqWUUiqPNBCrvBCRiSJiRGRsL2/ncyLy25zXj4rIN3pzm6pzIrJERM7v5rp75fexN4hIkYgsFpH98p0XVZg0EBc4EZksIn8WkfUiUi8iq0TkryKSiN4/X0SWdPK5rpafEx3grurkvadFpCXaTo2IvCIiZ/ZOyXqfiJRi78F7TXaZMeZUY8xNecvUTkTfzdH5zsdA0Bv7WkSOF5FM7jJjTAv26U4378ltqf5DA3HhewRYB0wDyoEjgccB2cX0LsLevOBCEXE7ef86Y0wZ9rnA9wJ/EpGpu7itfDsHWGCMWZrvjKgB717gRBHZJ98ZUYVHA3EBE5Eh2AB8uzGmxlirjTG3R2fZPU1vOnAM9ikzo4BTu1rXGJMBfg64wMxO0vqiiLzSYdkkEQlEZGL0+tdRDb5ORBaKyNk7yNs1IvKPDsueFpErcl4fICKPi0i1iKwUkRtEJL6DIn8Y+HtXaeY0f54X5a9BRB4RkSoR+b6IbIxaIr6Q8/nzoybWb4rIumidH+TmY2flFpFZIvKYiGwSkS0i8vdo+WvRKk9ErRK/6mJflYjIj6JtVIvIAyIyPuf9p6M83RflYamIfKirnZRTpq+KyOroM7eIyJAojVoReTu39igiMRG5SkTejcowT0QOyHk/LiK35uzDb3ay3WNEZH70+aUicqmIdPsEU0TOFJHXotab10TkjJz3tmsREpHfZPdpV/taRJZH5ZofLfdF5NDO0shZtlxsS9No7LPO3eiz9SJyHoAxphZ4Cfhgd8unBg4NxAXMGLMZeBP4lYicKyIzenKg6sRnsTXEh7E17Yu6WlFs0/cXgDTwWier/B6YLiIH5Sw7H3jaGLM8ej0fOAgYhG0i/o2IzNiVjIvIcOAZ4H7sAyGOBN4LfGsHHzsE6M5tPs8EjsY+4Woi8AKwNNrOBcAPcwMd9lm844HJUT5OB76W836X5RaRUVE5nom2NRK4EcAYc2D0+fcZY8qMMRd2kd/bsA+APyLKSzUwV9q3cJyHvZ96JfZJSXeLSMkO9sGEKL+To31xMTaoZG+dej/w65z1vw6ci31a0SjgWeDvIlIRvX8ZcBrwHmBSVNbWZxiLyP7Y3+DNwDDgA9hHkn5qB3lsJSJHYn+Dl2Fbb74N3Csih3fn8zvZ158DvgwMBv4CPJJTrh2luRZ7chtEaZYZY+7OWWUB9jepVDsaiAvf8cDTwFeAV4ENInJlh4A8SUS25U7Y2mwrEUliD3J3RYvuBObI9oNhLo8+vxr4EHCmMWa7vmZjzFbsvXoviNIX7MH/rpx17jTGbDbGBMaYPwKvR+XZFecCrxljfmmMSRlj1gA3RMu7UgXUdiPt64wxW6ITn4eBtDHmf40xGWPMo9h7Xx+cs34IfN0Y0xQ1e99EtB9gp+X+FLDEGHODMaYhKku7loAdEREHW+YrjDFrjDEN2N/GdOCwnFX/ZIz5lzEmBO7ABuR9d5B0E/CdKD+vYU++XjLGPG+MCbAP49hHRCqj9S8AbjTGvB21zlyLvWfzB6L3z43eX2KMacKeqOTeT/d/gD8bYx6M9tPb2BOGHX2fuS4A7jPGPBp9T38D/gp8upuf35E7jTH/McaksCdJTdiTit1Viw3uSrWjgbjAGWOqjTHfNsYcgq2xfAO4ipwDP7DMGDModwI+3yGpjwFl2AMq2NrIRqBjrev6KI3hxpj3GGPm7iB7vwY+GdWeT4zydz/YgCEi14rIoqjpcBtwILb2sysmAUd1ONm4C1uj7MpWYKc1GWwffFZjh9fZZeU5rzcaYxpzXi/HPnquO+WeiH14wq4aBiSBd7MLjDH12O9yXM5663Leb4hmc8vQ0cYoaGd13A/Z8mbTGNchDyF2P2TzMDZ6nZuHjTnpTQI+0eH7vBpbu+6OdtuPLKX9PthVy7Mzxt6MfyXR97ubKuiDT3dSvU8DcR9ijGk0xvwGW8M6qIcf/yy2v/cNEVmPrfEOBv5bOh+01R1PAM3Y2sL5wB+j2g/YRxleiG32rYpODl6j60Fm9dgn4eQanTO/AvhHhxOOymhgWVdeAXapKXwnhndo5p2I3Z+w83IvZ8c10509hWUT0IINZACISBkwnL37/OZVHfLgYPdDNg9rotfZ90uxecxaAdzV4fusMMZ09wlV7bYfmZyz/Z39nqDrfZ2bb8F2Q2S/33bpikiM9uXa0XOaD8D+JpVqRwNxARM7aOgGsYOU4tEAmTOx/9DP9iCdGcBRwBnYAJ6dDsPWKOfsSv6iWtA9wJewj/y7K+ftCiCDDRyOiHwaWzPsig8cIiKzo3J+kfYH2nsAT0Q+LSLJqOY5WURO2UGaDwAn97hgO+cA3xeRYhGZjG12zfYF7qzcvwOmiR3sVRJ9ryflvL+eHQTqnH1+nYiMjk4IfgC8Dby4h8rXHb8BviEiU6MWkcuxzzf/W/T+b4Gvi8gUESnGNt/nnoT9HPi4iJye89ueISLH9WD7Z4rI+0XEFZFTsb/BbD/2K9gTptOi38oZwLEd0uhqX39aRA4ROwDv60BJTrl84CSxAxOLgOuB3AGD67GDtdqdJIhIOfb/7aFulk8NIBqIC1sKe7Z9P7ZJaxNwBXCxMebPPUjns8DLxpi5xpj1OdPrwJ+j93fVr4HjsM3juYHgbuygpyXY2tEMdnDyYIx5GhtQHsM2iY4A/pXz/nrgBOxI6OXYZue/YmtBXfktcGAULPekFdgyLcOW8TFsoIGdlDsa0HM8dqDZamADkDui+HLgWhHZKiK/7GL7X8UGhJewzaajgA9Gfbl7y83YS3KewJbhROzAp2yf/A3Yy+yex+6nldj9BoAx5g1sS8pXsN/3Rmxw7VbXhTHm39gxCbdgfws3AecYY56P3l+KHXB1B/Z/5xTgvg7JdLWv7wB+HKV7FvABY0xN9N7vscH0ZWxT+Ers95zN1zvYk4wXoyb37OCzTwBPGWMWd6d8amDR5xGrfk1EPgccZYzp1mjcbqR3PnaglF4P2g+JyHLs9/u7na3bgzSLgDewJ0tv7al0Vf8Ry3cGlOpNxpjbgdvznQ81cEWjync0LkANcNo0rZRSSuWRNk0rpZRSeaQ1YqWUUiqPNBArpZRSeaSBWCmllMojDcRKKaVUHmkgVkoppfLo/wHGG0fRgoUpmgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x223.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values3 = est3.shap_values(X)\n",
    "shap.plots.beeswarm(shap_values3['Y0']['T0_1'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5. Other Inferences\n",
    "#### 2.5.1 Effect Inferences\n",
    "Other than confidence interval, we could also output other statistical inferences of the effect include standard error, z-test score and p value given each sample $X[i]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>point_estimate</th>\n",
       "      <th>stderr</th>\n",
       "      <th>zstat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>ci_lower</th>\n",
       "      <th>ci_upper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.372</td>\n",
       "      <td>0.146</td>\n",
       "      <td>2.540</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.416</td>\n",
       "      <td>0.163</td>\n",
       "      <td>2.553</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.291</td>\n",
       "      <td>0.170</td>\n",
       "      <td>1.709</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.410</td>\n",
       "      <td>0.150</td>\n",
       "      <td>2.733</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.584</td>\n",
       "      <td>0.152</td>\n",
       "      <td>3.834</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.553</td>\n",
       "      <td>0.139</td>\n",
       "      <td>3.974</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.430</td>\n",
       "      <td>0.124</td>\n",
       "      <td>3.472</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.605</td>\n",
       "      <td>0.154</td>\n",
       "      <td>3.921</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.439</td>\n",
       "      <td>0.127</td>\n",
       "      <td>3.471</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.494</td>\n",
       "      <td>0.162</td>\n",
       "      <td>3.048</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   point_estimate  stderr  zstat  pvalue  ci_lower  ci_upper\n",
       "X                                                           \n",
       "0           0.372   0.146  2.540   0.011     0.131     0.613\n",
       "1           0.416   0.163  2.553   0.011     0.148     0.683\n",
       "2           0.291   0.170  1.709   0.087     0.011     0.571\n",
       "3           0.410   0.150  2.733   0.006     0.163     0.656\n",
       "4           0.584   0.152  3.834   0.000     0.333     0.834\n",
       "5           0.553   0.139  3.974   0.000     0.324     0.782\n",
       "6           0.430   0.124  3.472   0.001     0.226     0.634\n",
       "7           0.605   0.154  3.921   0.000     0.351     0.859\n",
       "8           0.439   0.127  3.471   0.001     0.231     0.647\n",
       "9           0.494   0.162  3.048   0.002     0.227     0.760"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.effect_inference(X_test[:10,]).summary_frame(alpha=0.1, value=0, decimals=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also get the population inferences given sample $X$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Uncertainty of Mean Point Estimate</caption>\n",
       "<tr>\n",
       "  <th>mean_point</th> <th>stderr_mean</th>  <th>zstat</th> <th>pvalue</th> <th>ci_mean_lower</th> <th>ci_mean_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>3.368</td>      <td>0.061</td>    <td>54.777</td>   <td>0.0</td>      <td>3.267</td>         <td>3.469</td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Distribution of Point Estimate</caption>\n",
       "<tr>\n",
       "  <th>std_point</th> <th>pct_point_lower</th> <th>pct_point_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>1.729</td>        <td>0.683</td>           <td>6.074</td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Total Variance of Point Estimate</caption>\n",
       "<tr>\n",
       "  <th>stderr_point</th> <th>ci_point_lower</th> <th>ci_point_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "      <td>1.73</td>          <td>0.675</td>          <td>6.067</td>    \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<econml.inference._inference.PopulationSummaryResults at 0x1376c935828>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.effect_inference(X_test).population_summary(alpha=0.1, value=0, decimals=3, tol=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.5.2 Coefficient and Intercept Inferences\n",
    "We could also get the coefficient and intercept inference for the final model when it's linear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>point_estimate</th>\n",
       "      <th>stderr</th>\n",
       "      <th>zstat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>ci_lower</th>\n",
       "      <th>ci_upper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X0</th>\n",
       "      <td>5.969</td>\n",
       "      <td>0.229</td>\n",
       "      <td>26.053</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.592</td>\n",
       "      <td>6.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X1</th>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.216</td>\n",
       "      <td>-0.277</td>\n",
       "      <td>0.782</td>\n",
       "      <td>-0.415</td>\n",
       "      <td>0.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>-0.363</td>\n",
       "      <td>0.219</td>\n",
       "      <td>-1.660</td>\n",
       "      <td>0.097</td>\n",
       "      <td>-0.722</td>\n",
       "      <td>-0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X3</th>\n",
       "      <td>0.271</td>\n",
       "      <td>0.212</td>\n",
       "      <td>1.280</td>\n",
       "      <td>0.201</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    point_estimate  stderr   zstat  pvalue  ci_lower  ci_upper\n",
       "X                                                             \n",
       "X0           5.969   0.229  26.053   0.000     5.592     6.346\n",
       "X1          -0.060   0.216  -0.277   0.782    -0.415     0.295\n",
       "X2          -0.363   0.219  -1.660   0.097    -0.722    -0.003\n",
       "X3           0.271   0.212   1.280   0.201    -0.077     0.620"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.coef__inference().summary_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>point_estimate</th>\n",
       "      <th>stderr</th>\n",
       "      <th>zstat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>ci_lower</th>\n",
       "      <th>ci_upper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cate_intercept</th>\n",
       "      <td>0.456</td>\n",
       "      <td>0.218</td>\n",
       "      <td>2.09</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                point_estimate  stderr  zstat  pvalue  ci_lower  ci_upper\n",
       "X                                                                        \n",
       "cate_intercept           0.456   0.218   2.09   0.037     0.097     0.815"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.intercept__inference().summary_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Coefficient Results</caption>\n",
       "<tr>\n",
       "   <td></td>  <th>point_estimate</th> <th>stderr</th>  <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X0</th>      <td>5.969</td>      <td>0.229</td> <td>26.053</td>   <td>0.0</td>    <td>5.592</td>    <td>6.346</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X1</th>      <td>-0.06</td>      <td>0.216</td> <td>-0.277</td>  <td>0.782</td>  <td>-0.415</td>    <td>0.295</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X2</th>     <td>-0.363</td>      <td>0.219</td>  <td>-1.66</td>  <td>0.097</td>  <td>-0.722</td>   <td>-0.003</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X3</th>      <td>0.271</td>      <td>0.212</td>  <td>1.28</td>   <td>0.201</td>  <td>-0.077</td>    <td>0.62</td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>CATE Intercept Results</caption>\n",
       "<tr>\n",
       "         <td></td>        <th>point_estimate</th> <th>stderr</th> <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cate_intercept</th>      <td>0.456</td>      <td>0.218</td> <td>2.09</td>   <td>0.037</td>   <td>0.097</td>    <td>0.815</td> \n",
       "</tr>\n",
       "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"
      ],
      "text/plain": [
       "<class 'econml.utilities.Summary'>\n",
       "\"\"\"\n",
       "                  Coefficient Results                   \n",
       "========================================================\n",
       "   point_estimate stderr zstat  pvalue ci_lower ci_upper\n",
       "--------------------------------------------------------\n",
       "X0          5.969  0.229 26.053    0.0    5.592    6.346\n",
       "X1          -0.06  0.216 -0.277  0.782   -0.415    0.295\n",
       "X2         -0.363  0.219  -1.66  0.097   -0.722   -0.003\n",
       "X3          0.271  0.212   1.28  0.201   -0.077     0.62\n",
       "                       CATE Intercept Results                      \n",
       "===================================================================\n",
       "               point_estimate stderr zstat pvalue ci_lower ci_upper\n",
       "-------------------------------------------------------------------\n",
       "cate_intercept          0.456  0.218  2.09  0.037    0.097    0.815\n",
       "-------------------------------------------------------------------\n",
       "\n",
       "<sub>A linear parametric conditional average treatment effect (CATE) model was fitted:\n",
       "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n",
       "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n",
       "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
       "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>\n",
       "\"\"\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.5.3 Doubly Robust Average Treatment Effect Inference\n",
    "\n",
    "For the case of `CausalForestDML`, the estimator also fits a doubly robust average treatment effect at fit time. This inference result can be accessed as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.53511397])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est3.ate_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>point_estimate</th>\n",
       "      <th>stderr</th>\n",
       "      <th>zstat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>ci_lower</th>\n",
       "      <th>ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATE</th>\n",
       "      <td>3.535</td>\n",
       "      <td>0.079</td>\n",
       "      <td>44.671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.405</td>\n",
       "      <td>3.665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     point_estimate  stderr   zstat  pvalue  ci_lower  ci_upper\n",
       "ATE           3.535   0.079  44.671     0.0     3.405     3.665"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est3.ate__inference().summary_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Example Usage with Multiple Continuous Treatment Synthetic Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. DGP \n",
    "We use the data generating process (DGP) from [here](https://arxiv.org/abs/1806.03467), and modify the treatment to generate multiple treatments. The DGP is described by the following equations:\n",
    "\n",
    "\\begin{align}\n",
    "T =& \\langle W, \\beta\\rangle + \\eta, & \\;\\eta \\sim \\text{Uniform}(-1, 1)\\\\\n",
    "Y =& T\\cdot \\theta_{1}(X) + T^{2}\\cdot \\theta_{2}(X) + \\langle W, \\gamma\\rangle + \\epsilon, &\\; \\epsilon \\sim \\text{Uniform}(-1, 1)\\\\\n",
    "W \\sim& \\text{Normal}(0,\\, I_{n_w})\\\\\n",
    "X \\sim& \\text{Uniform}(0,1)^{n_x}\n",
    "\\end{align}\n",
    "\n",
    "where $W$ is a matrix of high-dimensional confounders and $\\beta, \\gamma$ have high sparsity.\n",
    "\n",
    "For this DGP, \n",
    "\\begin{align}\n",
    "\\theta_{1}(x) = \\exp(2\\cdot x_1)\\\\\n",
    "\\theta_{2}(x) = x_1^{2}\\\\\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DGP constants\n",
    "np.random.seed(123)\n",
    "n = 6000\n",
    "n_w = 30\n",
    "support_size = 5\n",
    "n_x = 5\n",
    "# Outcome support\n",
    "support_Y = np.random.choice(np.arange(n_w), size=support_size, replace=False)\n",
    "coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
    "def epsilon_sample(n):\n",
    "    return np.random.uniform(-1, 1, size=n)\n",
    "# Treatment support\n",
    "support_T = support_Y\n",
    "coefs_T = np.random.uniform(0, 1, size=support_size)\n",
    "def eta_sample(n):\n",
    "    return np.random.uniform(-1, 1, size=n)\n",
    "\n",
    "# Generate controls, covariates, treatments and outcomes\n",
    "W = np.random.normal(0, 1, size=(n, n_w))\n",
    "X = np.random.uniform(0, 1, size=(n, n_x))\n",
    "# Heterogeneous treatment effects\n",
    "TE1 = np.array([x_i[0] for x_i in X])\n",
    "TE2 = np.array([x_i[0]**2 for x_i in X]).flatten()\n",
    "T = np.dot(W[:, support_T], coefs_T) + eta_sample(n)\n",
    "Y = TE1 * T + TE2 * T**2 + np.dot(W[:, support_Y], coefs_Y) + epsilon_sample(n)\n",
    "# Generate test data\n",
    "X_test = np.random.uniform(0, 1, size=(100, n_x))\n",
    "X_test[:, 0] = np.linspace(0, 1, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Train Estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "est = LinearDML(model_y=GradientBoostingRegressor(n_estimators=100, max_depth=3, min_samples_leaf=20),\n",
    "                model_t=MultiOutputRegressor(GradientBoostingRegressor(n_estimators=100,\n",
    "                                                                       max_depth=3,\n",
    "                                                                       min_samples_leaf=20)),\n",
    "                featurizer=PolynomialFeatures(degree=2, include_bias=False),\n",
    "                cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<econml.dml.dml.LinearDML at 0x1377f9aa898>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = T.reshape(-1,1)\n",
    "est.fit(Y, np.concatenate((T, T**2), axis=1), X=X, W=W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "te_pred = est.const_marginal_effect(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb, ub = est.const_marginal_effect_interval(X_test, alpha=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<econml.dml.causal_forest.CausalForestDML at 0x137061c6cc0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "est2 = CausalForestDML(model_y=GradientBoostingRegressor(n_estimators=100, max_depth=3, min_samples_leaf=20),\n",
    "                       model_t=MultiOutputRegressor(GradientBoostingRegressor(n_estimators=100,\n",
    "                                                                              max_depth=3,\n",
    "                                                                              min_samples_leaf=20)),\n",
    "                       cv=5,\n",
    "                       criterion='mse', n_estimators=1000,\n",
    "                       min_samples_leaf=10,\n",
    "                       min_impurity_decrease=0.001,\n",
    "                       random_state=123)\n",
    "T = T.reshape(-1,1)\n",
    "est2.tune(Y, np.concatenate((T, T**2), axis=1), X=X, W=W)\n",
    "est2.fit(Y, np.concatenate((T, T**2), axis=1), X=X, W=W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "te_pred2 = est2.const_marginal_effect(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb2, ub2 = est2.const_marginal_effect_interval(X_test, alpha=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Performance Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uv/56Vq1axerVq7n99tvZuHEjmzdv5tprr6WxsZFQKMTZZ5/N5s2bAXC5XNx6662ceOKJnH/++bz55pucffbZLFiwgMceewwwftRnnHEG69atY926dbz66qsAfO1rX+Pll1+msbGR22+/HU3T+OpXv8pJJ53EmjVr+MUvfpEa73nnnUdZWdl4fKQKhUKhUCgUimMItV42mIj18nGfrTSd7/51BzvbB8a1zxUzyvn2ZStHdM2CBQvQdZ3u7u7UsQ9/+MM88sgjnHDCCaxbtw673V50f7t27eLhhx9m06ZNWK1WbrnlFh544AFWrlzJ4cOH2b59OwBer5fKykp+8pOfcNttt7F+/fqcvgKBAGeffTY//OEPufLKK/nmN7/JM888w86dO7nuuuu4/PLLqa+v55lnnsHhcLBv3z6uueYaNm/ezH//939z22238fjjjwNw9913U1FRwVtvvUUkEuH000/nwgsvVBlIFQqFQqFQKKYoar18bK+XlTicoiR3QZJ89KMf5eqrr2b37t1cc801qd2FYnjuuefYsmULJ510EgChUIj6+nouu+wyDh48yOc+9zkuvfRSLrzwwmH7stlsXHzxxYBhvrfb7VitVlavXk1TUxNg1ML57Gc/y7Zt2zCbzezduzdvX08//TTvvvtuyv+7v7+fffv2KXGoUCgUCoVCoRgWtV4ef5Q4TGOkOxYTxcGDBzGbzdTX17Nr1y4AGhoasFqtPPPMM/zv//7viH7sUkquu+46fvCDH+Sce+edd3jqqaf46U9/yh/+8AfuueeeIfuyWq2pzEcmkym1I2MymYjH4wDcfvvtTJs2jXfeeQdd13E4HAXH9eMf/5iLLrqo6PeiUCgmnrimYzGrqAOFQqFQ5KLWy2NfL0spp+x6Wc3+U4yenh5uvvlmPvvZz+akn/3e977HD3/4Q8xm84j6PO+889i4cWPK7O7xeGhubqa3txdd17nqqqv4j//4D95++20AysrK8Pl8o34P/f39TJ8+HZPJxO9+9zs0Tcvb70UXXcRdd91FLBYDYO/evQQCgVHfV6FQjA/tXlVDVKFQKBRTl6N9vazLqbteVpbDKUAoFKKxsZFYLIbFYuHjH/84X/7yl3PanXbaaQX7uOmmm/jiF78IwOzZs3nttddS51asWMH3v/99LrzwQnRdx2q18tOf/hSn08knP/nJVBrg5E7J9ddfz80334zT6czop1huueUWrrrqKh555BHOOeccSktLAVizZg0Wi4W1a9dy/fXX84UvfIGmpibWrVuHlJK6ujoeffRRAM444wx2796N3+9n1qxZ/PrXv1YWRoViknAHItS4bJTa1RShUCgUiqnBsbRe1qWcsutlke2reyyzfv16mcwilGTXrl0sX778CI1IMRbUd6dQjIxQVMNpG34n9dUDvcysdDK3pnQSRjUxCCG2SClzswQcAwgh7gE+AHRLKVcN0e4k4HXgainlkMW98s2PCoVCkUStucaXSEzDbh2ZZXO05PvuhpojlVupQqFQHAdIKen1R4pqG4pqRbcdb2KaTjimHZF7H0XcC1w8VAMhhBn4IfDUZAxIoVAoFMWjT2HjnBKHCoVCcRwQimkEo8WJrlBMo9cfneAR5aetLzSlJ82pgJTyJcAzTLPPAX8Euodpp1AoFIpJRp/C05wShwqFQnEc4I/ECRVpkQtFNfoCUbQjMHs1u1VSqrEihJgJXAn8fJh2NwohNgshNvf09EzO4BQKhUKBLmVOGY6pghKHCoVCcRwQjGgEo/Fh28U1nZgm0aWRmGYyicQ1OvtVptRx4A7gVinlkLsBUsq7pZTrpZTr6+rqJmdkCoVCoUBKmJrSUGUrVSgUiuMCfyReVCxfunWx1xelvix/3aWJoNUTmtKuNkcR64GHEunda4H3CyHiUspHj+ioFAqFQgEYeQCkBMSwTScdJQ4VCoXiOCAYLS7mMJTWZrKT0iiX0vFBSjk/+bcQ4l7gcSUMFQqFYuogIeFWOvXUoXIrnQKYzWYaGxtZuXIla9eu5Uc/+lGqlso//vEPhBD8+te/TrXfunUrQghuu+02wKizsnHjkFnKR8S9995Le3t76vUNN9zAzp07x9xvU1MTDz744LDttm3bxqmnnsrKlStZs2YNDz/88JjvrVAc7wQi8QzhV4h0y+FkupWGohrdviOTIfVoQwjxe+A1YKkQok0I8WkhxM1CiJuP9NgUCoViojiW1sv6EG6lR3q9rMThFMDpdLJt2zZ27NjBM888wxNPPMF3v/vd1PnVq1dnfOEPPfQQa9eunbDxZP/Yf/WrX7FixYox91vsj72kpIT77ruPHTt28OSTT/LFL34Rr9c75vsrFMczgWicmCaJa/qQ7dLFYSiq448MH6c4HrR4gkzR2Pwph5TyGinldCmlVUo5S0r5aynlz6WUOQlopJTXD1fjUKFQKI4GjqX1skQWnPOO9HpZicMpRn19PXfffTc/+clPUlmM5syZQzgcpqurCyklTz75JJdccsmI+r3//vvZsGEDjY2N3HTTTWiahqZpXH/99axatYrVq1dz++23s3HjRjZv3sy1115LY2MjoVCIs88+m2RxZJfLxa233sqJJ57I+eefz5tvvsnZZ5/NggULeOyxxwDjR33GGWewbt061q1bx6uvvgrA1772NV5++WUaGxu5/fbb0TSNr371q5x00kmsWbOGX/ziFwAsWbKExYsXAzBjxgzq6+tRmfQUirERjBiib7iMpdmup72TZM1TLqUKhUKhKJajeb386F/+AhIOHTo0JdfLKuYwnb9/DTrfG98+G1bDJf89oksWLFiArut0dw+Wp/rwhz/MI488wgknnMC6deuw2+1F97dr1y4efvhhNm3ahNVq5ZZbbuGBBx5g5cqVHD58mO3btwPg9XqprKzkJz/5Cbfddhvr16/P6SsQCHD22Wfzwx/+kCuvvJJvfvObPPPMM+zcuZPrrruOyy+/nPr6ep555hkcDgf79u3jmmuuYfPmzfz3f/83t912G48//jgAd999NxUVFbz11ltEIhFOP/10LrzwQubPT4XL8OabbxKNRlm4cOGIPkOFQjFIOKYRT2R6CUU1yhzWwm2zxKE7EGFebemEji8QiR+xuooKhUKhGCFqvTzm9fJz511M3RRdLytxOEXJrn3y0Y9+lKuvvprdu3dzzTXXpHYXiuG5555jy5YtnHTSSQCEQiHq6+u57LLLOHjwIJ/73Oe49NJLufDCC4fty2azcfHFFwOG+d5ut2O1Wlm9ejVNTU0AxGIxPvvZz7Jt2zbMZjN79+7N29fTTz/Nu+++m/L/7u/vZ9++fakfe0dHBx//+Mf57W9/i8mkjNwKxWgJpLmGDmc5zD7f45t40ba/2z/h91AoFArFscfRvF6ORmN84bO3TLn1shKH6Yxwx2KiOHjwIGazmfr6enbt2gVAQ0MDVquVZ555hv/93/8d0Y9dSsl1113HD37wg5xz77zzDk899RQ//elP+cMf/sA999wzZF9Wq5VEenRMJlNqR8ZkMhGPGwvQ22+/nWnTpvHOO++g6zoOR/5U+FJKfvzjH3PRRRflnBsYGODSSy/l+9//PqecckrR71WhUOQSiKTFEY5QHHqDUeKajsU8MRs0vnCM3Z0DE9K3QqFQKCYAtV4el/Xyj++8Y0qul5U5ZorR09PDzTffzGc/+9nUjyrJ9773PX74wx9iNptH1Od5553Hxo0bU2Z3j8dDc3Mzvb296LrOVVddxX/8x3/w9ttvA1BWVobP5xv1e+jv72f69OmYTCZ+97vfoWla3n4vuugi7rrrLmKxGAB79+4lEAgQjUa58sor+cQnPsFHPvKRUY9DoVAYBKKDlsPhyllkn9clHPaGJmRcAJub+xgmR45CoVAoFBmo9fLErZeV5XAKEAqFaGxsJBaLYbFY+PjHP86Xv/zlnHannXZawT5uuukmvvjFLwIwe/ZsXnvttdS5FStW8P3vf58LL7wQXdexWq389Kc/xel08slPfjKVBji5U3L99ddz880343Q6M/oplltuuYWrrrqKRx55hHPOOYfSUiNeac2aNVgsFtauXcv111/PF77wBZqamli3bh1SSurq6nj00Uf585//zEsvvYTb7ebee+8FjIxQjY2NIx6LQqHIdCvNjilMJ67pxLXc9GnbDw8wt2b84w5bPUE6vOFx71ehUCgUxx7H2nr5hhtv5hMfu3rKrZdFtq/uscz69etlMotQkl27drF8+fIjNCLFWFDfnUJRHC/u7eFwn2H9m1Zu57zl0/K2GwjHePydjrzn3reoljk1JeM2prim87f3OjJcXpN88IQZlNjGtncphNgipczNEqDIS775UaFQKJKoNdf4EY5pBCJxbBbTkAnixot8391Qc6RyK1UoFIpjnHTL4VBupUNZFbe394/rmHa0D+QVhgqFQqFQHMskDXNT1T6nxKFCoVAc4xSbrXSoc95gjFZPcNzGtLdr9HEaCoVCoVAcrSQqSzFFtaEShwqFQnEsE43rxNLiCOOaJFYgA8xwyWreOzw+1sOBcCxjTAqFQqFQHC8kZ7+pGtp3RMWhEOIeIUS3EGJ7gfPXCiHeTfx7VQixNu1ckxDiPSHENiGECpRQKBSKPKRbDZMUEoHDlbkYqfUwXkCEegOxovtQKBQKheJYQrmVDs29wMVDnD8EnCWlXAP8B3B31vlzpJSNKumAQqE4XtH1oWeX9DIWScIFROBQMYdJtrZ6C16fTmd/mI7+/JlIPcHosNcrFAqFQnEsIpVbaWGklC8BniHOvyql7Eu8fB2YNSkDUygUiqOEZk+Qtr7C1rx8SV9Co7QcAvjDcV7e14s2jCjd1TFAjz+S91yfEocKhUKhOE6RJC2Hckq6lh5py+FI+DTw97TXEnhaCLFFCHHjERrTmHG73TQ2NtLY2EhDQwMzZ85MvY5GJ3YBtXv3bhobGznhhBM4cOAAd955J8uXL+faa68dcV933HEHweDgAvUb3/gGs2fPxuVyjeeQFQpFFoFInFcPuOkP5nfVzGc5LCQCh4s5TNLji/DGQXfB832BKB39Ydz+/M+wvoAShwqFQqEonmNpvRwITO318lEhDoUQ52CIw1vTDp8upVwHXAL8ixDizALX3iiE2CyE2NzT0zMJox0ZNTU1bNu2jW3btnHzzTfzpS99KfXaZrMRj+cu7MaLRx99lCuuuIKtW7eycOFCfvazn/HEE0/wwAMPjLivbHF42WWX8eabb47ncBUKRR5CMY24JnlxXw+ReK64C+axHI425jCdJneQd9u8ec/t6hgAwBOI5Li9hqIa4Vj+WESFQqFQKPJxTK2X08ThpR+YeuvlsVUZngSEEGuAXwGXSClTW9VSyvbEf7uFEH8GNgAvZV8vpbybRKzi+vXrp57tNg/XX3891dXVbN26lXXr1lFWVobL5eIrX/kKAKtWreLxxx9n3rx53H///dx5551Eo1FOPvlkfvazn2E2mzP627JlC1/+8pfx+/3U1tZy7733snXrVu644w7MZjMvvfQSS5cu5eDBg1x++eV86lOf4sYbb+Rzn/sc7733HvF4nO985ztcccUVaJrGrbfeylNPPYUQgs985jNIKWlvb+ecc86htraWF154gVNOOeVIfHQKxXFHUuj5w3Fe2dfLOUvrMZlE6rw/T0KafDGDMU0nPsIMotsPD2A1m1g+vTx1LBCJ05JIWqPphgtpjcueOq9cShUKhUIxHhyt6+XL338h1TU1/OWJpzn55JOxmKeWrW5Ki0MhxBzgT8DHpZR7046XAiYppS/x94XA98bjnp988pM5xy6adxH/tOyfCMVD3PLsLTnnr1h0BR9c9EH6wn18+R9fzjj3m4t/M6px7N27l2effRaz2cx3vvOdvG127drFww8/zKZNm7Bardxyyy088MADfOITn0i1icVifO5zn+Mvf/kLdXV1PPzww3zjG9/gnnvu4eabb874n+jJJ5/khRdeoLa2lq9//euce+653HPPPXi9XjZs2MD555/Pfffdx6FDh9i6dSsWiwWPx0N1dTU/+tGPUtcqFIrJI5TmNto1EOH1Q25OWzj4/2Gx2UpHYjVMZ2uLF38kzolzqjCZBLs7faQbC3v9meLQo1xKFQqF4qhHrZdHv15+9G9PUZNYL09Fq9URFYdCiN8DZwO1Qog24NuAFUBK+XPg34Ea4GdCCIB4IjPpNODPiWMW4EEp5ZOT/gYmkI985CM5OxrZPPfcc2zZsoWTTjoJgFAoRH19fUabPXv2sH37di644AIANE1j+vTpw97/6aef5rHHHuO2224DIBwO09LSwrPPPsvNN9+MxWL8dKqrq0f83hQKxfiRLfSaeoNYzR5OmldNXNOJxHNdOPMJwUJJaophX5cffzjOSfOrOdDjzzjX64+wlLLUa2+B2EiFQqFQKEbK0b5enoL5aI6sOJRSXjPM+RuAG/IcPwiszb1i7Ay1c+G0OIc8X+WoGvXORzalpaWpvy0WC7o+uMALh4308FJKrrvuOn7wgx8U7EdKycqVK3nttddGdH8pJX/84x9ZunRpzvGEKFcoFEcYXZd54/f2dfmxmAQL6vIHuOcrWTEWcQjQ0R/miXc7iGfFGPZmZSxVZSwUCoXi6Od4Xi+nz3JjXS+rbKWKUTFv3jzefvttAN5++20OHToEwHnnncfGjRvp7u4GwOPx0NzcnHHt0qVL6enpSf3YY7EYO3bsGPaeF110ET/+8Y9TP9qtW7cCcOGFF/Lzn/88Ffjr8RiVSMrKyvD5fGN9qwqFYgQM5Qq6q8PH2819ec/FdUk0y6I4WrfS7H6zCUS0lPCMxnX84YlLGqBQKBSK45fJWi9LORi7P9L1squsDL9/cL089aShEodHBVdddRUej4fGxkbuuusulixZAsCKFSv4/ve/z4UXXsiaNWu44IIL6OjoyLjWZrOxceNGbr31VtauXUtjYyOvvvrqsPf81re+RSwWY82aNaxatYpvfetbANxwww3MmTOHNWvWsHbtWh588EEAbrzxRi655BLOOeccAP7t3/6NWbNmEQwGmTVrVkE/cIVCMXrylalIp1AResgVg+MhDguRtB56Q8pqqFAoFIqJYTLWy7qUgCQUjROL6yNeL3/605/h6g9dwRXvvxCAr3/t1im3XhZT0Zw5Uaxfv15u3rw549iuXbtYvnz5ERqRYiyo705xvNPiDvLK/t5RXXvusnoaKhyp15v299LsDg5xxehZPr2ME+ZUsafTx5YC1sx0PnjCDEpsY4t6EEJsScSoK4og3/yoUCgUSdSayyCu6fSHjNh5IQTlTgsWU/G2tkhMy8gi7rSZxzzfDUe+726oOVJZDhUKheIoJRgbvYtmjuVwjDGHQ+H2GxZDlalUoVAoFEcz6dETUkoCeWoJD0W2SW4q2uiUOFQoFIqjlELF7Iu7dlBYtvUFJ7T+oCcQRdclXpWMRqFQKBRHMXqWmotrOlqeePtCZHtsTkVxOKXrHCoUCoWiMGOx9oVjGtG4zpbmPg71BsZxVLnEdYk7EE254igUCoVCcTSSLQ4BopqO0zR0OY0k2ZfLKZiSRolDhUKhOEoZi+WwayBCq6djTH2MhAM9fkawuapQKBQKxZQj3zwWi+s4rUWKw+zXU3BeVOJQoVAojlKCw2QrHYrJLkbfMkHJbhQKhUKhmCz0POowpuvousRkGr6uYbblcQpqQxVzqFAoFEcrE5lEZrzJVwMxHy/u6eZrf3xvgkejUCgUCsXIyedWijRcS4shx610CpoOlTg8wrjdbhobG2lsbKShoYGZM2emXkejE5u8Yffu3TQ2NnLCCSdw4MAB7rzzTpYvX86111474r7uuOMOgkHDMhAMBrn00ktZtmwZK1eu5Gtf+9p4D12hOO4Jx7Rj0k3zQE+Agz3+Iz0MhUKhUEwhptJ6+e67fsqpJ67lpk9fl2oTjRcnDn/6kzsz1ssf/dAVU269rMThEaampoZt27axbds2br75Zr70pS+lXttsNuLx0buNDcejjz7KFVdcwdatW1m4cCE/+9nPeOKJJ3jggQdG3Fe6OAT4yle+wu7du9m6dSubNm3i73//+3gOXaE47pmsWMHJptsXZkGd60gPQ6FQKBRTiKmwXt7y9tvMm7+Ae375Cx7646P84te/TbWJ63p+q2IWd/3kx4TS1sv/8vkvTbn1soo5nIJcf/31VFdXs3XrVtatW0dZWRkul4uvfOUrAKxatYrHH3+cefPmcf/993PnnXcSjUY5+eST+dnPfobZnBkUu2XLFr785S/j9/upra3l3nvvZevWrdxxxx2YzWZeeuklli5dysGDB7n88sv51Kc+xY033sjnPvc53nvvPeLxON/5zne44oor0DSNW2+9laeeegohBJ/5zGeQUtLe3s4555xDbW0tL7zwAueccw4ANpuNdevW0dbWNumfo0JxLDOWeMOpSjimMRCOs6Cu9EgPZUojhLgH+ADQLaVclef8tcCtiZd+4P9IKd+ZxCEqFArFhDPZ6+UXX3yJeQsX0dx0iH+++sN87OPX8YlPfpr/+5UvsXPnDqSm8d3vDr1e7uxo54OXXkR1TQ1/eeJp3nfmWUgpp9R6WYnDLJo//omcY2WXXEz1xz6GHgrReuNNOecrrrySyg9dSbyvj8Of/0LGubm/u29U49i7dy/PPvssZrOZ73znO3nb7Nq1i4cffphNmzZhtVq55ZZbeOCBB/jEJwbfQywW43Of+xx/+ctfqKur4+GHH+Yb3/gG99xzDzfffHPG/0RPPvkkL7zwArW1tXz961/n3HPP5Z577sHr9bJhwwbOP/987rvvPg4dOsTWrVuxWCx4PB6qq6v50Y9+lLo2Ha/Xy1//+le+8IXMz0WhUIyNoynesFh6fBEAJQ6H517gJ0ChCeYQcJaUsk8IcQlwN3DyJI1NoVAcBxyP6+XPf/HL+MIxnn/2aR7921PU1Nby/e98i/eddTZ33nU3Qf8A5515+pDr5dv+539S1yaRQP8UWi8rcThF+chHPpKzo5HNc889x5YtWzjppJMACIVC1NfXZ7TZs2cP27dv54ILLgBA0zSmT58+7P2ffvppHnvsMW677TYAwuEwLS0tPPvss9x8881YLMZPp7q6umAf8Xica665hs9//vMsWLBg2HsqFIriORbdSpPicKFyKx0SKeVLQoh5Q5x/Ne3l68CsCR+UQqFQHAEmc72cz230heef48kn/sbP7rwDGHq9XCj5TCw2tdbLShxmMdTOhcnpHPK8papq1Dsf2ZSWDu6cWywWdH0w0DUcDgNGhqPrrruOH/zgBwX7kVKycuVKXnvttRHdX0rJH//4R5YuXZpzXIjhU/UC3HjjjSxevJgvfvGLI7q3QqEYnmNSHPojmIVgdpXzSA/lWOLTwJEPYlEoFMcUx+N6OZ84lFLym/sfYvGSJQCUO6xYLaa86+VCIYk33TS11ssqIc1RwLx583j77bcBePvttzl06BAA5513Hhs3bqS7uxsAj8dDc3NzxrVLly6lp6cn9WOPxWLs2LFj2HtedNFF/PjHP07tcmzduhWACy+8kJ///OepwF+PxwNAWVkZPp8vdf03v/lN+vv7ueOOO0b7thUKxRCEYsdezGG3L0KNy4bFrKam8UAIcQ6GOLy1wPkbhRCbhRCbe3p6JndwCoVCMc4Mt17u7OxCSjnq9XK+DOHnnnc+v/rFz1Lr5S2J++dbL+tIXK4y/H4fAkPE/tf3vj3l1stqBj4KuOqqq/B4PDQ2NnLXXXexJLE7sWLFCr7//e9z4YUXsmbNGi644AI6OjoyrrXZbGzcuJFbb72VtWvX0tjYyKuvvprvNhl861vfIhaLsWbNGlatWsW3vvUtAG644QbmzJnDmjVrWLt2LQ8++CBgWAkvueQSzjnnHNra2vjP//xPdu7cybp162hsbORXv/rVOH8qCsXxzTFpOfSFqSuzH+lhHBMIIdYAvwKukFK687WRUt4tpVwvpVxfV1c3uQNUKBSKcWa49fJFF13EqtVrOP/8C2hvb8+4tpj1sp5HHf7rrV8nFotx5inred+GdXz3O98G8q+XpYRPfPLTXP2hK7ji/RfRfriNH/1/P2TXrl1Tar0spmLxxYli/fr1cvPmzRnHdu3axfLly4/QiBRjQX13iuOFjv4QDeWODBeVP2xuJa4dO8/vuK7zncd2cNaSOn56zVpKHGMTiUKILVLK9eM0vClHIubw8QLZSucAzwOfyIo/LEi++VGhUCiSHAtrrlA0ntpYNZsELrtlRJ4q3mAUbZgCw1aziXKnNe+5aFzHF44Z99ejaCYbAC67Bbt16LjJsZDvuxtqjlSWQ4VCoRgjxRa/HS27O3y094dTr2OaPmHCMKbp3P7sXnZ1DExI/4Vw+6PoEhpKBMTDw19wHCOE+D3wGrBUCNEmhPi0EOJmIcTNiSb/DtQAPxNCbBNCKNWnUCiOezQ9/W854rl7GF2YaFO4kWTwnJB62vFMNF0nrk/sumIoVEIahUKhGCP7u/2smFE+IX1H4hpdA4ZYmllpJGqZSJdStz9Kjy/CC3u6WT59Yt5TProTmUpnOYLDtFRIKa8Z5vwNwA2TNByFQqE4KsgWblFNp2QE1xbjbanpsmDyxvTLkzGH2ccBwjGdcEzDbBLYLCbMQqBJiaZLzEJQYp9Y+aYshwqFQjEGpJTs7fIVNWmMhsN9IXQJHf1h+kOGO8pE1jj0hqIAtPWFaPVMnlDr8RkCeI6lf9LuqVAoFIrjh2xxqOkSrUgLXb54w0IUcj3NuL2UKYEos2yH8YSJU9MloaiGPxInFNWIxvUhLZPjhRKHMGGLOsXEob4zxVQhENUIRrVUjb50dF3S7g2Nqf+WNIG2r8vICByMTlymUm8wEQ9hErx+MG8ekwmh2xehssRKhTZ591QoFApF8RzNay8pJVqe8UeLDNEYgTbMex/IcitFIhLt0pvruiQ+kpsNw2i+s+NeHDocDtxu91H9gz/ekFLidrtxOBxHeigKBYGIIdRa+3KtbM2eIAd6/KPuOxrX6UyLNTzYGyCm6aNyK231BNm0v3fYdt5gDLNJsH5uFe8d7scfmZySGT2+CPUuO7aoshwqFArFVONoXy/rUuYG9wGxIuMOR2KxK2RlzOxCkhxQ+vHYOMYajna9fNzHHM6aNYu2tjZUjaejC4fDwaxZs470MBSKlHhq9YQ4cW7muT2dPgbCMXRdYjLlxh8MR7s3lLFbGdckB3sChGIjF4fP7e5ib5ef5dPLqS61FWznDUWpcFo5dUENbxzysKXJw1lL60d8PwCzFkIzD1/QXpeSXn+ExVVmhDz2SnQoFArF0c7Rvl7WdEm4wNxZYjPnjRFMJxrXiWnFCTeLSeTNPhqJaSmroEULoZlsSGHGbBI4Eu0jcT3lVjqSvgsxmvXycS8OrVYr8+fPP9LDUCgURylJy2EwqtHrj1DrMkow9PgieAJG/F6XL8z0iuFFUjYteWL+9nb5KHOM7NEdjesc7AkA8G6bl7OHEHveYIxKp5X6cgcL6kp545CH9y2uwzxCcWvSolQN7KG3qnHYtt5gjJgmmeXMdc1VKBQKxZHnaF8v7+/28+4hT95zp82qYV5t6ZDXv3bAzaHeQFH3qi61cvHy6TnH/7Gnm3av4Q00//CLuMtXMlC2kGnlds5bPg2Ax99tZ2AIj525NSU0Lqotahyj5bh3K1UoFIqx4A8PPsTTxdzeRHwgMKq4w7iW6VKaxBeOp7KXFsvBHj9xXeKwmtjW6h3SLag/FKOyxLAsnrqgBm8oxp7OkZe1sMX6KQs0IfTh3VJTyWisk1s+Q6FQKBTHB4EhBNfhrDlaSpnTPhQrPsRiIJy/bXrpDJMew6wbG6KxRNxjOKYxEJqcUI6hUOJQoVAoxkB6TF4yu2coqmVk+mzrG7k4bPeGCwalF+nZkmJPlw+b2cR5y6bR7YvQWUBcarpkIBSjssQo4LusoZwKp5Und3Ty5PYOXj3Qy872/iFdXpLYY15MeoyyYMuwbZNlLOZZ8u/qKhQKhUIxFoYSh+3eUCpOUNclm/a7+ceenoy5LhApPuQhrsm8ieMCiWNCj4PUMeuGd1HSXXWkG78ThRKHCoVCkYdiBBAMPuzBmDw8gSj7un0ZsYKBiIY3GB3R/fMluBkNUkr2dPpYWO+icXYlJgHvtHrzth0IxZBApdMQh2aT4P2rp6NL2HTAzePvdnD/Gy1sOjB8RlF71LhHuf/gsG17fBFKbWaqxOiT9ygUCoVCUQjfEOIwpkm6fRFDGB7opcUTpD8U4+0Wb6rNSEtI+bKsh92+MKGosa4wSSMruCkhDpMF77sGpkZohRKHCoVCkYd2b3jYrGxGDaJMEdnkDrC/O1fkZLuteIPRvO3AEKaHR2FtzEeXL4I3FGPZtDJK7RYW15fxTlt/3sxrfYkah0m3UoDVMyv4yoVL+d7lK/n6+5czu8rJ1pa+YT8bW8zIOmqP9mGPDG0R7PZFaCgdecIehUKhUCiKYbgSUK19QV7Z30urZ3Du3d/tp8UdJBLXRlxeYiBRlzjVf5o3kUlPlIxKupXGjb6V5VChUCimMO5AZMidRiBvmYe9nT7CsVyrY7rYi2s6r+zvLRjcPpRL6UjZ02nEPi5pKAOgcXYl/aEYze5cy2R/osZh0nKYjhACl93CurlVdPsidOSJh0whdWyxwfjB8kBh66GUkh5fhJmOWME2CoVCoVAUots3tKiKa3rORm42+7r8eUNA3jjkzlvHeDgGwplzWnrfSXdSc8pyaMQ4ZlsbjxRKHCoUCkUeBsJxvIGhBUu+GIZCms4diKbSaG9p7mMgFKfHF8nbR7OnuIxo2fgjcQ5m1VXc0znA9AoHFQnBt3x6OTazkZgmm76EOKwoyRWHSVbPqMAsRN7rk1jjvoySFGWB1tROaTaBqEYopjHbPrr3rFAoFIrjl3BMS2XjLkRgFLWBk8Q0yesHRx4Pn56UptcfyYhZNCUStZm1wXCTbO+iI4kShwqFQpGHgVAMb2joOMGRFIiX0gh6b3YHOJA2kWVb8GKaPqrspgB/f6+DX71yiDcT6bpDUY0WT5Cl08pSbWwWEytmlLP9cH8qziFJfyhKqd2C1Vx4aiixW1jSUMY7bd6CRYHtWYXshYxTFmjK27Yj8V7n27zDvT2FQqFQKDLo8UVSSc0KMZK5Oh/pWUaLJd2tNLssVXKz1KQPjrttnPIMjAdKHCoUCkUWui7xR+IpS1ohRjrhHOgJpIRbkpYsK2FbX2jE2UjBKCS/t9uPScBfth3m3TZvKjHO0oayjLZrZ1UQimns68q0MnqDMaqGsBomaZxdiS8cL7hba495c45V9+/EHu3LOd6ecE+d75w6E6NCoVAojg56/RH84fiQCWOGylQ6UQSjGlrClag1WxymEtLEjJ1joHuKJKMBJQ4VCoUiB184jpQMm2F0pBNOjy+SqmeUxBOIZcQmNLtH517Z2R8mEIlz2doZzK0p4ZHNbby4t4cSm5nZ1SUZbRfVl2E1i5yEON5gLOV+moHMVKvLGsqwW0xsa80VezCYqTQdkx5lRvdLOQKxo89PrV3HZRmFIlYoFArFcU2v35inh4oLHKvlcDRICb5wDHeWSykMZimFwaQ045RmYFw4ouJQCHGPEKJbCLG9wHkhhLhTCLFfCPGuEGJd2rmLhRB7Eue+NnmjVigUxzr9CXeQQEQb0p1kvHYjm3uNXcVIXMtb+L4Y9nUZiWdWTC/nE6fOo77cTkd/mCXTyjCJzEygZpNgdlVJRmyjlBJvKJqTjKY0eDinVqHVbGL1zAq2tw/k/XySmUqzyRaIZf5DdLs9LHAOjsMSD0B8amRsUygUCsXURdMlnoAhrnr8heeNI2E5BBgIxWnNk+TGnBaDny4UpwpH2nJ4L3DxEOcvARYn/t0I3AUghDADP02cXwFcI4RYMaEjVSgUxw3plryh4g7HK7NYUqS1ekKj3j3c2+1neoWDMocVh9XMJ0+fz8oZ5Zy6oCZv+7k1JXT2h4nEjR3NYFQjpsmMMhZmLUx93xZKg+051zfOriQa19nVOZBx3KyFMWuFJ+mkQJzZ9Q9cPW/TGbExr2Sw/ZyuZ3H8+qyUq41CoVAoFPnwBKKpMIyh3DKPmDgMx3LiDYGMBG1mJQ4zkVK+BAyVAugK4D5p8DpQKYSYDmwA9kspD0opo8BDibYKhUIxZtIDyb0F4g6jcT3HRXT094vTF4jmxB8WSySm0ewOsLh+MLbQZbdw7clzc1xKk8ytKUWXpGo6eRPvuTIt5rDeswWTFqEk3IXQMyfXebWlVDitbEsrEgxgy+NSmo1Jj+KI9NActAOwICEO+2WIJ92dRKetAaHqHioUCoWiMOmupN5QrKCnjz8y+mylY6HFE8SfZxM5GXMIYNamTqxhkiNtORyOmUBr2uu2xLFCx3MQQtwohNgshNjc09MzYQNVKBRHH4UmknTLYV8g/67eeMcw7O700TXKgPSDvQF0CYunuYq+ZnZVCYLBhDjJ+Mqk5bDcf5CSkGExFDJOSbgr43qTEDTOrmRvly/lhgtgL+BSmo9DQQcA1U4PD0Xf5PVnH2L9I2a2u/MLWoVCoVAokvT6B+dMKTNfJ4nG9VFlGx0PCm0um5Rb6ZjIt3Ushziee1DKu6WU66WU6+vq6sZ1cAqF4uimxRNM1R5MZyA0KPy8oQL1+cZZHB7qDYzak3Jvlw+b2cTcmuJFldNmpr7cniqlkZzEKp1WLPEAtd53MtqXhnJdS0+aVw3AW02DDiD5MpUW4lDITqUljjQNUPP8dj7ykoZ5fogVl3yy6D4UCoVCcXySLQbzJaU5Ui6lQ5GZkEaJw5HSBsxOez0LaB/iuEKhUBSNJxDNqSkYiMSJpwX+9QdjyDyq7UhkPyvEvm4/C+pKsZhG9kifW11KiyeILiXeYBSrWVBiM1Pp25fjRloa7siJA6wutbF4movNTZ5Uyu5i3Ep7dR/3RV9lh+NZ5jlDLH2+ias26diWOZl/chAaVo/ofSgUCoXi+GIgHCMcy7QI5qt3OJXm6iQq5nBsPAZ8IpG19BSgX0rZAbwFLBZCzBdC2IB/SrRVKBSKojHEYWbylHSXUoC4LvHlmVymym6k2x/BE4iyeFrZ8I2zmFtTQiSu0zUQxhuKUem0IZA52UkBTFoER6Q35/iGeTUMhOPs6fQhpIYt7s9pk6RLH+A3kVf4RvhPvBrfTyBu45SBfdhffofoScuYflI/ftdsMJlH/F4UCoVCcfzQm0cIegKR1EZlkqkoDjOzlU69mEPLkby5EOL3wNlArRCiDfg2YAWQUv4ceAJ4P7AfCAKfTJyLCyE+CzwFmIF7pJQ7Jv0NKBSKoxZdl/SHovjCMXRdYjIZ3urpLqVJvIEY5Y7MEg9TZcLZmyhhsaS++HjDJHNrSgFodgfxBmNUllgpDXVgKhAgXxpqJ+zIdM9f2lBGucPCm01uTqjVcmoiJtkU38+90U1YMHG2ZRlLYyfyw65VlCxsI3D9JYg5FZTsfZ7eqrXYR/xOFAqFQnE8kc+FVNPBHYhQX+ZIHZsqG7npZFgOtalnOTyi4lBKec0w5yXwLwXOPYEhHhUKhWLEDIRjaDpoSHr9EerLjcmkP0+MoTcUZQ6Z8XxTRRzu6/ZTXWqjxjVySVVVYqXMbqHFE8QbijGj0kFZoLlge1eoHXfV2oxjZpPgpHnVPL+7G19f5mdyWDfqGc40VbHM1MAFlhVcZF1JhXTQ85e3WVbpYv4qDa1qJlUDuwDwlc6ldsTvRKFQKBTHE73+/KKqxzcoDnVd5ngDHXGkjpCDc+VUdCs9ouJQoVAcm4RjGg7r1HYN9KRlIT3sDaXE4UAecdiXJ+NY8Ailxk6nPxTjQI+fdXOqRnW9EII5NSUc7PETiMSpdJiM2MICWOJ+bNF+oraKjOPr51Xzwp5u3mr2sLwBWnQ3f4u9yxatmUbzbD5rP48ak4uP2k4CTcf5xxdZ+N4BTlw1nTqbkWi6PNCMJqwEHNNH9V4UCoVCcXwQiWt5N3IB2vpCSAndvjC9/ijxcSo5NV6kWw2N18qtVKFQHAf0BaPUlzkwm6Zurbq+4KA47OgPc0Li73y7jN5g5s5eKKplJK05EkTiGr97rQkhBKcUKHRfDHNrStnRbhSyn24eKOgWmqQ0dDhHHFY4rSyfVspL3ha8lc/xrt6KEysfsKzhfOsKwMhlIzQN5x+ex7qrmUcbL2Tb6lO4XBhVicqCzfhLZiFVvKFCoVAoChCN67zd7C143u2P4i5gVZwKZIvDqWg5nOoJaRQKxVFIMKoRjE4Nt8tCeAKZhe4DkTiRuJaT/QwgENFSdZKicZ2DvYWTrkwGupQ8/FYrHf1hrjlpDtPKHcNfVIC51YPusrNk1xAtDcoDzTlZS6WUnNMQIeI4wG6tmyusJ/BD54f5oG0dLuGgLWTjs1vn0n/PP7DuaiZ46WncM/985pcYyYDMWoiScBcDJXMBsJnV1KRQKBSKTFo9Qf72XjuHegNHeiijxiynvjhUlkOFQjHuhKIawahGWVYSl6lEX5Y1sKM/RIXTVrD9zo4BvMEonf1hjrDRkL+918HuTh+Xr53B0oaRZylNZ3qlA4tJENcl0019w7a3xP24gq34S+fQGtzOK72/Z23lRVzgiPBk5AQ6DmzgjOXtlAjD7VaX8IvmBryalYN6BTvP/ADT18wltsvEPKfhTlMWbEEAvtI5mE0CixKHCoVCccyh6ZJoXMdpG7mHyOYmD3u7juzG7HiQbTlE6pj0GLpp6qyXlDhUKBTjTjCqTckMYUkGwrGcOITD3jBQ2A12Z8L18kghpaTVE+T1Qx62tXp536LaMbmTJrGYTMyqKqHZ7afaNnzgvpSSbs/T/Ll3Py2h9ygxVyLiAzhiIf5lro1/2zmPX7U08K8LDiMEPN/soMcd58ZlXnZNv5i/dNVSf8AQ5knLYVmgBV2Y8Ttn4rAoYahQKBTHGu3eEJub+1hc72L59PIRXSul5OBRbC1MJ0ccAiY9qsShQqE4tglG4wSjUzd2rC+Q68bR1R/GZZ+aY97a0seLe3vo9kWwmU2cvrCGi1c1jFv/6+ZUMk12YykiRPSe6Cu8ph2gzFzB+fU30lh5EdMG9gO7mOGI8pEZvTx4uJ7X+8pYors58ZHHOdkCi5ZU425YS5lV5/62euwmnRkO43soDzbjd85EmqzYzFM3TlWhUCgUIyMS13jjoIe2vhBghHGMlP5Q7obu0YopjxupWYsQt5QWd308PHyjMaLEoUKhKJpoXMdWhGUn6VY6WWi6HFHyG08ecRjXJYd6g+M5LFrcAd5s8nDxqum47KN73PrCMR7Z0kZDuYMrG2eyZlYF9nHOBHtGQ5wZpta856SUvKe3sdTUgF1YaTTPYYGpjpNKNtBVfREAZcHBaz8wzcMbfWX8ZbeN777yOK6gn6qLS5nX/Sw2LchlDaXU22L44yZMAkxahNJQB+21pwNgNU9Nga5QKBSKkdPhDaeEIeQvFzUc+WoaHq2YZH7LYbGYYxPvWqvEoUKhKJptrV42zK8etl0wquGYxIQ0+7p9zKx0Fh3jmB1vmCSZdGa82Nrq5e0WL83uINefNm9UtQhbPYZg/WDjDObUFLezOFJcwZacY7qUbNNaeDz2Li3SzbXWUzjHuowTLUbSGGIDDIQ60U1WLPHBycos4POunVT9+QmcsQh971/CutKn0UxWqgd20jrtPE6u8qXaV/n2IJD0uxYCFLX5oFAoFIqjA3fWZmy+clHD0eM/hsShHqM9bOOhw7Vc0eBhYWl4RElpzDHf8I3GiJqFFQpFUcQ1nYM9fnqHeUhruiQS1wmNwnIY10YnzlrcQQ70FB+P0BeYnKK47kCUCqeVYFTj5y8dpK0vSFzT2d05wB+3tLFxS35rXTotniBmk2BGpXNiBil1XKH2wZdSsjnexHfDj/Gz6AuEiXK97XTOsCzJubR6YHeG1TDJgmdfpIwYb116JueUPkufazHN0y7CEfNSEu7MaFvb/x4RawW+kjkA2IrxbT3OEULcI4ToFkJsL3BeCCHuFELsF0K8K4RYN9ljVCgUCsj11InrEv8IcxIUKnh/JHhlfy//8/Qe9nePzoL3Znucr+2axxvecv7UYeQNMI+g1qGyHCoUiknHG4xSWZKbtbMvGEOXsLfLR+0QFrBQzBCFo0lI0+2LUOawjCjLaSiq0euPEojGWTOzAtMw7qVGyYrxtRAWwu2PMLemhPOWTePeVw/xy5cPYhKCSFxHABK4dPWMITO3tXhCzKhwFMzgKaSGFKN3xSwJd2HSIkgpEUIghOD5+C40dD5tO4MN5vmYRf57OyI92KO5GU5DHz4bW8DDZ7wPEzFXcmDWlQipIzv+Rs3AToJOo9C9Neanwn/QcCkVxvdmVZlKi+Fe4CfAfQXOXwIsTvw7Gbgr8V+FQqGYNKSUeWP8+0OxokMtwjENf3hqJLjzhWM8u7OLuK5zz6ZDvG9RLResmFbUvBWJazy2rZ2trSZWuILU26O85K6gL2Yu3q1USszxiU/Mo2ZhhUKRQbM7f9xdcvev1RMkHCtsFUzWN4xpcsRumgPhGK2e0PAN02jtM8Ybiuoc9g5/bb54w4kgrut4gzFqXXbqyuzcfNZCljWUs3pmBdedOo+rT5oNgDtQeMdQ0yWHvUFmp9UizKYk1FnwXDE4A81siu/nu+HH8Erjs7zJfjbfc1zBqZaFBYVhEiGN79uytxXHIy+ApiPLSpgbfQWTjLF3ztVoZgdxSwkDpfOpHtiZqpNY078dgaS3cnWqPyUOh0dK+RLgGaLJFcB90uB1oFIIMX1yRqdQKBQG/aEY8Ty1n7wFQjvyMZXiDZ/f3U1c1/mXcxZxyoJqXtnfy13/OFDUuuLv2zvZ1urlg3MifGtJC1c0eNARvOSuKNqt1BQbwBcZvuTUWFGzsEKhyKDdG8rr3ulOuJNqOkO6U6S7k47UtdQXjqfEXrEkY/IADvQM725RKN5wvOkLxJBATalhhS1zWLlmwxw+tG4WSxvKqCszrK9DTSpdA2FimhxSHNpiXiyjcDPRZIx3+v7Of7vv4DfRVxDAgDTEdYVwYhpGFKZjefcAzgeextzrhUgUZ7iLKt9eOmpOJWyvS7Vzl6/AEe2jJNwFGC6lfsf0jDY2uwOshd+voihmAun+vm2JYxkIIW4UQmwWQmzu6emZtMEpFIrjg+x4wyQjSUpTqI/xpt0bGnJDu9cX4a0mDxvmVzO9wsnla2dy3alz8QSjPLVj6E3amKbzTquXxtmVfHB2AJOAGY4oy1xBXuitwKQV9x4rH/8tA7fdgeafWNdSJQ4VCkUGgYSbZjbpD+j93X70ApXg07OUBkaYlGYgFMPtj6asj8MRjml0p+0qdvSHh3RnjcZ12r0TnwYaBi2CSXGYTXXieD6XmyQtCeE7p6qwWLJoIZzR3uEHJCW1fVtB6sT0MHcfvIknun6CS9j4rO08/t1xOXNMWXUTpU6d522m97xSsFvrmztxbnwBbc40Ap+8FEoczOx5Bc1ko7NmQ0bbvvJlSATVAztxhnsoDXfgTrMaCgG2qlkpF1PFqMn3Aeb8DyulvFtKuV5Kub6uri7PJQqFQjF6Cm1+jiQpzWRYDg97Q/z0hf38YXPhPABP7ezEYjZx7rJpqWNLG8o5eV41O9r7h9x43tkxQCSus25uVUadw7Nr+umI2DnkHVy3SCnZ2T5AJOGh5Y4e5pXe31P12vPM/cvfqK2ejXBOUA6CBCrmUKFQpNB1wxW02xemocKROh6Ja/jSfP6DUY22vhBzanJFS7o4LFbkJUneo9UTYmlD2bDt2/qCSQ9FwPBWPNgTYPWsipy2nf1hXj/onrQSG+6EwK4uEJ9pt5gptVuG3BVt9QRx2S1UlhSOwbRoYRwRD77SeUOOxxTp5lDfCyzWQnTWnMKaigtZFo1xUsyOyCPGSkPtzGt/AlfYSFYTcM5gwLUgo41t03s4nnyD2NI5hK4+F6wWHBE31QM76Kg9Dc2cOYEZrqXzDNdSJBJBb8Wq1HmrWSDKcwxcipHTBsxOez0LaC/QVqFQKCYEd4FEMv2hWCrOfSh0PX/M4ngipeSJ9zoAQ8Tt7hhg2fTyjDYtniA72gc4b3l9TqzkqQtr2HSgl9cOuHn/6vze+1tb+qhwWplfW4q5Y/D9nFo1wL2t9bzUaeWSRcZY/r69k1f297JhgQNXwz/Y0vdXVreYmPNQlPCiBTg+/XFME1zySVkOFQpFinDcEE7dA5k7dfl2//Z25U+nHMoQh8ULsbimp9qnu4oORb74xIO9fmSaYoxrOluaPTy/u3tSay+6A1HsFhOlQySbqS6x4hlit7G1z4g3HGoCtWghHFF3wfNRPcybnj/zvy1f4CfR5/D7dzG99zXOqPoQJ2mu3L6lZE7HU6w8+Cts8QEOzLyCsLWSOV3PkqHEAW3ONKInLSN0zflgNSbM6b2bkMJCR80pecfjKV+BM+qhwfMW/a4FxC2u1Dmr2QzlMwq+F0XRPAZ8IpG19BSgX0rZcaQHpVAojh90XRaMLdR08BWRtK4vGM0bszha3jrkoT0rN8GeTh+HegNcsno69WV2Hnu3PcO9NK7r/P29Dlx2C+9bVJvTZ2WJjVUzK3iryZM3H8NAOMa+Lj8nzK7EJESG5dBhlpxW7eNNt51ITOMfe3t4ZX8XpbVvsNPydd7q+wunmM7g1j8JItNm4v3Eh+nTJ96upyyHCsVRTFzTC2axHA1JYecORDIKy+fb/evxR/LeP91aGIgUL8YG0iyTPf4I4ZiGY4hi75G4RtdArotoIKKxubmPWFzHE4ziC8ezNc2k4AlEqHXlt8olqXHZaXbnzzwWjMTp9Uc5cU7VkPexaCFMehShx5CmQQtjVA+zpe+vvOn5M0GtnyWWuVxuPY1ppnIItTO769lUMpl0SsJdTPe8QU/lWpobLkIzO9CFmcVtf6K2/116Xauw7G0lvmIe2ux6tNn1qWttUS+13nfprl6fIfoyPpfyZczreAKzHqW3Yk3GOZOzAuz5r1MMIoT4PXA2UCuEaAO+DVgBpJQ/B54A3g/sB4LAJ4/MSBUKxfFKXzDKULquPxijfJjM5ONZwsIbjPLnbYexW0x86vT5zK4uQdMNS12ty8apC2qYWenkly8f5IU93Vy0sgF/JM6DbzTT7Any4XWzsFvyr0net6iWd9v62dLcx+lZAvKdVi8SOCExl5tkpkvtOTVenu+t5ME3W9jX7Wf17BI6yp4j4G9gQ/n1nLX4RNr/6QkGVp2Ixb+dr79g5uulTXz81Hnj9tlko8ShQnEU4wlGqS9zDN+wSMKJ3TJNNxLQ1JcbfeezHEoJ3lAsp6xFKDY6t1JfePCBKaXhMrqovrBraVtfqODEs69r4usADUevP8rMYWoTVpXYeKfVS1zXsZgyRXZrn7G7OVQyGiE1TJph5XVEPYQc01KuOnE9wqbeh5hdspLTa67mTPfeDDHokEHyfTvlgUMAtNWfg2ZOfP/lK/E7X2fm4ecJbmnHsu8w/v/zQfQZmZPgdPdrIKCj9tSCY45bShkonYsrdJi+8qWZ70dZDYtCSnnNMOcl8C+TNByFQqHIYbgMnv2hWIbvez7GM96wOeGRZDYJfvPqIT51+nwOe0P0+CP888lzMZsE82tLOXFOFS/v66Gh3MFTOzrxR+JcvX42a2dXFux7VlUJ82pK2HSgl1MW1KQ21qWUvN3Sx+wqJ3VldoQey/HAWVwapqHsMC1iM8saruGj6xbg1/6XR//RSf/+XvRFEvfZ70dKye/fLUUHzllWn2cU44dyK1UojmLGu5h7uktoeqKXQuUW8sUChDIS0hRvOfRl1TEaqqSFrstRF6CdDLSEO02NK38ymiTVpTYkxg5qNq19QQQws6qwwDRrg5ZTPdTKSz2/46HWbyGlpMRSwc0Lf8nVs7/HIvO0HCthVZ5almCIw5Cthqg1LeZCCFrKzqLjaTuW/YcJffCMHGFoi3qp79tKb8VaotbcmM90Dk2/lD1zPoZuyhyDqUKJQ4VCoTgWGM7qly9j6fbD/Rkbxb3+cRSH7gA2i4lbzl6E02rmnk2HeGZnF/NqSlk+fXAj+uJVDdgtZh7e3IouJTeeuWBIYZjkfYtq8QZj7OwYSB3r6A/TNRAZtBrqme/ZLyM8HHuD0Myf4qjaynlrDY+tSir52qZ7+b/P/JgDh4xMqLvbenm7v5Rrl+jMGiJJ3XigLIcKxVGMIdqGT9xSLOn+8t2+MFBBMBonFM2f3jl7ZzAU1TKseaERWA6zs5d1DYSJxLW8bhyvH3QXDHSfCngT7jQ1pfmT0SRJZiz1BKLUZFlgWz1BGiocBd1YwHAp9ckwz8R28FzXg0SIssR1KlE9hN1cgstSDYAzUToinZpSW86urJAa5cEWeitWZx73+tAf3Ea438aMMwY4sCYraYyUzO94AikEh+vOGPI9A0TsNUTsWZlRhQlLpRKHCoVCcSxQjOUwnVZPkHfb+nnvcD9zq0uYV1s6rnkCmnqDzKkuobrUxg3vW8AvXz6INxTj/asbMsI/Su0Wrlo3i21tXj6wZvqwrq9Jlk0vp7rUxjM7O4nENBbVu3i7pQ+zSbAmkSQv6VKqSZ0X43v4S2wbQaKcaV3MmTXXojvmous6c395G1WHdvCTUz/OrsMhZs/W+Mt73cx1hrlinoo5VCgUQzDelsN0cdjri6LrckgR1pdl8QplBWNrOsPGDiYZyLIc6hLeae2ncXYlNsugk8PbLX00uUdWC3GySWYgLVTGIklSHLoDURanHdelpLUvyJqZlUNe3x7YwX+FNhIlzomWBayd9a/UO+bltCsJd2e8tpgF5U4rJkGGmC8NtWPWowyUzs9ob27pxhQIE7v2DMpiG1nU9gi75/5zKsaxpn87lf79NDVcRNQ29JgLEbZVUzLB6bkVCoVCMfHENJ2B8NDrk4FQDF2XmBIumNsP9wOG12WTOziu83woauQoWDnTcMesKrVx81kL6fZF8lrhVswoZ8WM8pzjQ2ESgktXT+fPWw/zp62HAaOm0IoZ5ZTYDLllTrMc/iO+h9mmaq62ncRsUzX4DoGvCdPT7+J64w0Of/TTaIvP4eCOTh56qwVfROfWZZ2YTbNG+SkUjxKHCsVRSlzT8YWLSwddLOFYeoYuiScYHbLUQn8omvFwzxdjGIwWJw59eSaS/d1+Wj1BVs+qYFGdi73dPnZ35M+SOpVwJ1xhhnMrLXNYsJhEjntury9COKbnjTf0xdz0x7qYVbKCWZZaTrMs4hzLMmaYKmkxV5P9KQo9jiPqyTjmslsQgNViIpL2nZcHmgAYSJbFCEXAaSe+ZiH+RTORJQ588mrKdz7IorY/sW/2R7BoYeZ2PoXfOZOu6pOG/WwKEXRMo8SqpiSFQqE42ukLRIdNBKdLI2NphdNKqyeYs9k8nrR4gkhgXk1p6li500q5szirYLGsqrOw9tx6WqKlHOj20+IJcuZio4asO9LK612/4Qa5kBJh46uOi3GRmbTOsv0gJS+/QeS0NXRf8mFOikue293F3i4/Z8+CRaWTU6dZzcQKxVFKKGa4cIZjOs4hyiWMtM90ugbCeArEG4JhGewPxahKWMBCeVxAApF4ykJW8L5RjZiWfyaJxHU2N/Wxq2NgRNlPx8rO9gGmVzryxuaFoho2iykVdJ6NOxDFZjHl1EPKxiQEVaW2nHIWLYnA+dnVg5a0/lg3r7s38k7/05RZarl5wd04tTjX2gZLRjgibmLWzN1OR6QXZKZbcJnDGJfVnC0ODxFwTCNuKcGy4xDOR18m+PGL0OZMQ5Y4qC61UVG/gZY+D3M6nkxkHY1h1sIcnHEZiNGHsYcc9ThsKgxeoVAojnaG2lROpz8Yo8Jp5b2E1XCiaHYHMAmYPcGxetM8mwk6ptFQXkNDuYPTgZDm45muX7Cl73FswkarrYql5gbKRG4ywfjyuYQufx+xE5fijPaAYxob5lWzo2OAq+b0w/iFYA6JEocKxVFK0soXjMbHTRxm1+jp9kWGje3rC0ZT4jBffEAxMQP5rIbZTKYwHAjFeOCNZk5ZUMNlazPj4HQp+dGze6lwWvjYhrl5ha/bH6Wm1FaURbe6xJYTm9HkDuC0mql12fFGu3jN/TDv9j8HwJqK8zil5iMIYcKiZSbtcUTdRC0LiaTVaCqJZLqUApQlYihsaWVIhB6jLNhKV/V6rG/sxPG3V9Fm1aPXJmIlxKBYDc46ncMxHzN7NwHQVncGIcfos6fpJitxR+2Q8ZUKhUKhmJp0+8LsaB/AZbdQarNw2Fs4oVw6/aEYLe4g3gm0GoLhpjqj0pkRojLelPsO4Ax3oSUSrUkp2dL3V17ufYCIHqSx8mLe7zid+QP7cq41N3Wi15YjXSXETloGgCvYSsgxjUtWT+eiVQ1UdL8wYWPPRolDheIoJSnkglGNmmHaFkuO5bA/PGSdIjDEYZL84nD4pDTZ8YZHmh0dA0jyZ2ntD8UIROIEInF++sJ+Prp+NksbMpMCuQMRGsqLKzFSXWqjyR1IuQdLKdnX7WdhfSkmIeiOHOS9gedYW3khp9Z8hArroAjLEYcRNycuq+flfT0pMe3Mijc0maA0Ef+QPlGWBdsQuoZ3MzjfeJXYsjmEPnIuJNrWlztwJMRbmd1CW/25mPUoJeFO2muHT0KjmR0Z2VXTCdnrcQxjZVUoFArF1KTVE6TDO3KXR28oSmvfxOYQiGs6bX1BTlkwXiulXKwxH7XedwGwx7wACCE4ENhCg2Mh59XfSL1jHuUDu3OuNbV1U/K7J4kvnEnoYxekjruCh+mpWodJmDAJgTU+eRna1WysUBylJMVhtqAbLXFNJ57l2jmcMATwpCXFCcXyxxwOx3CB65NNMjA+n9U0eezKxpm8fsjNfa81cd7yaZybqDuk6ZK+QIyVM4Yu55CkutRGJK4TjGqU2i3s8RwgVv0A5up5wA0sdp3CLQt/k8o8mo4lS2w5tAGq7UYNpGd2dBGLhFMTVRKXzULSGzbdclgeOER/sxPeaCV60jLCl54GifMWs2BG5aDYddktIATN0y8p6j0C+ErnUZlnYgQI22soKSIuVaFQKBRTj7a+4iyF2Rweol7xeNHuDRHXJXNrRu9SWhZoxl8yCynyzFNSMs3zJkLGade9/Mn/HKeGV1LpmM2VM7+GVThSXkRmPXNNYerxUvK7p5GlTsKXnZ55To9SEu4m6GzApEUx6ZOXoV2JQ4XiKCWUZjkcz/5GSl8wmrJ65RtLIJIpGJvdAerLHBmusNk1Do8k/kicpt4AVrPAG4yhS4kpzT006QK6eJqLtbMr+dPWNp7d1cW82hIW1LroD8XQpBw2U2mSpFvqwf79HIj9hV2+l7GUWaguNQrECyHyCkMAc5bl0GY2w67HKLeVcoHDwtZud07BXVdaWu50y2F5oAm5rIrgvPOo2LCEOWUOBkJRBkJxasvsWE2DbUsdFoTI6XpIfCWzKfcfyKnzBBCxVVE5Tq7RCoVCoZg8+oOxUYd9TLQwBFJZT+emJaMZKaXBw0gE/tI5Oecq/AeIhdv5Y2wb/4jvwY6FFaE9VDpmYzNlZuBOn/+snm7s9z0PAgLXXYwsyxWvrmArQWfDpFoNAVT0v0JxlJIeczie/Y2UuCZTbqH5xGG66AzHNDY39bGjPTP4PLvG4ZFkZ7vhUrp+bjWalDkF6t2BCBaTUQrCZjFx1bpZVDitPLm9EyllyhW1ephMpUmqS21Yq1/kcc+/ciDwFqXBC3B0/jsXTP/4kNeZtAhCZn7edouAiA98nZSH2ljrGiA77DGZjAbAahGIgQAlv30Ce28HPtcC4ivnU2K3UuW0Mre6lNUzK5ie5SJrEYKSEYg5KcxEreVEbFV5z0dsVTiUOFQoFIopgzcY5fF32znYM7QwafNO7dJSTe4AtS7bsAniCiIlzkgPFf6Dec7pbHNv5OuhP/FCfA9nWZbyX86rWGOZm7erpDg0x0PUbfwDpkCQ4CcuRtbk9zQqDR0GqWONB0Y39lGixKFCcZSSFF35MoSOhuxkNCOhLxAlGs91SwVDMMqEientlj4icZ393f6URVHXZY518Uiyvb2fmlJbqsZRdtY1tz9KVYktZU20mk2cv3wabX0htrcPpNxOa0szi9pn0x7aizfaRVWJDS24iJlczmfm/Zre1vNYUtsw5LVWs8hxKQWwZSV0qXRamVE5uHMpRMIlNB6B9zZifuUPlN21EUtzJ7GAOVXCwmEdfmoYyUQbtVWAMOUVhzFrGbrJOiKxqVAoFIqJIxzTeHFvDwOhOK8f9PDUjs5UiaZsDo/SpXQy0KWk2R0ck9XQHvNi0qM4Ij1YYwMZ58qCrXTFe5hrquE7jsu51nYKZcKBPerN21fSrbQ03M709V6mnxtEn144FtKkxygJd2GNT24JLyUOFYqjlPA4u5WOSRwGowXdUqU0xtjZH6ap19hh1CW822ZYD/3R+KS4luTj6Z2d7O4cfNgHI3EO9vhZNbMi5RaanZTGE4jm1C88YU4lDeUOntrRSbcvgtUsMix06bQFd/Jw67/z2+Yv8bpnIzaLiVLmUhq4lC6vibguWVzvKjjmUruZmZXOnGQ0APY8mdhmVjopT4ylxGrGgg5v/5bAG2/Rdn8rFi3M/HM7cU7T8JXMBkglnhkKl734+lBhm+EWG7HmisOkYFQ1DhUKhWJyicZ1XtjdTUf/4Hyi6ZKX9vZkuIq6/VGe2dlFf5aXTzimFV22YjKQUvL87i7+saebFk+QroEwoZjGvDHEG6YndSv3H6I30sLDrf9Oa3AHlQN7uNp6El+2X8hM0+D8Zo/25e3LFA9je+VdSvwdWOw6lVUeKvwHhry/K9g66ZZDNRsrFEcp4XG2HI4lsU1fMDqke6s/EuetpsxC7E3uACtmlOM/QlbDmKbzjz09mE2CT542jwV1LnZ2DKBLWDWjgnKnFYtJZJSZSLqNLqzL3IU0CcHFqxq499UmNjd5qHXZc8pYtAZ38ErvAzQF38FpLufsuutYV/kBIFHOIhhlf5cfsxDMr3XhDHUScuZaEGdVObGZzXi0XFeefGm6BbCg3sWOw/247GbYvhH/OwdofbkW+/RqQh85iVZLP5rZgTRZEQIcRSSHcRUQv/lICsCIrTL3nNU4pmocKhQKxeSytaWPjv4wHf1hZlY5WTenkvfa+unNk4xNl7Cl2cO5y6aljrV7QyOKPR8PpJQ89k47yxrKWNqQWdf33bZ+nt2VFHNdqeRr88ZgOXRGegHwyTCPex7gxa5d2E1OosH92GJRyJOkxhb3IaSWkcBGxKNUPPxXbNv2YpWlROrLMck49X1b6C9bVPD+rtBhopbygucnAiUOFYqjlKQ4jOuSSFwbc4240cYcgpGxNF2k/nnrYWZXOVk/z7AYbWnuy0k6IyW819afY4WbLJIlOATwu9ebufHMBexoH6CqxMqMSiO7WFVWDUJfJE5Mk3lrGy6ud7GgrpSDPYHU+WSiHoCdAy/ijrVw8fQbWFl2CTbTYBxfdamNg70BQlGNuTUllBDGFWrLKw5nV5UQjGp5y0LksxwC2M0m5te5sB16Hlpew3nS2VTZSqn78Bns8kbwpH13NospNaEOhcNiwmYRROPDrwySFsOkC2l6UH7SqlhiU9ORQqFQTBYd/SEO9AxapA73hYYVe539EVrcQeYkLHHF1jMcT1o8Qd445OGdNi+fP3cxlSXGfBuJafx9ewczKh1cd+o8mtxBDiTiJfPN2UUhJY5IDy/G9/DH6BbCxNjgOpNTGm5isXsL4C5wnY4t6iVir0n1M++BO7Bt20v4vBOZ17CJgH0GYVs1092vYY0NELPmF4BCj2OPevKemyjUVq1CcQSIaXoqDm80ROM6WpqWGw/r4Vgsh9G4To/PcL/UdMnbzX3saB901yxU4LbFE6TFc2SC2fsSou/DJ87CbjFx76tN7O/2s2pGRUrQVZdmikNPYje1xpUbTyiE4JKV0wGoLbNxwL+Z37V8ldbgdgDOn34dT1z5d/7z3M+xpC4z+2h1qY3+UIzOgTCLp5Vhj7pTu5Xp2C0m6srsuByWvG6lQxX4rXTvIPjYy+h1jZjXXk7DdRdgLnVgzbrGMYIiwcW4lkphIZo26WXEHQqRsiY6VSkLhUKhmBRims6bh3IFRzHLkrdb+ohrOrou6egfeW3DsbKluQ+b2YSuw8YtbeiJQT+/u5uBcJzL186kzGFl9cwKPtg4kw82zszx5CkGKSW2iAeTHiMsY8w31fJtx+V83LqB6ngUR6SAMEyQHnc47bHfU/n8c0ROXYX2vuU4ox4Czul0V69DIKnv21r0uErd2yE6sW6mShwqFEeA/lCMSHz0lrpsITcecYdjiTkEaE/ELLgDETQp8YaKi0PIV0twMkiKvvm1pXzy9PnENB1NSlbOHMwaVu2y4fZHU0I+GVtRqEzFjEoHl2zwcNj5Q/7Q9m18sV7CuiF+T503m0pnKTaLidMW1XLKgmospkERmmRxvQtHxIM15sOkZcY7zqpyIoQRz5hXHJrzCyzNH6Llf5+id3s5fn0DiMFHf7agLMalNEkxrqURWwXpKVOTbqQAMYsLabJit5gwF2OuVCgUCsWY2dbqHXX5iWBUY3v7AF2+cN4kdBNJNK7z7uF+Vs+s4ANrpnOwN8Cm/b10D4TZdKCXE+dWMad69PGFSXoiTTzU+i32eJ8C4ALLSr5ov4CZpiockR7qvNuG7SNZY9jS38e0vz9C9ITFRC4+mZJIFwABRwMRWzX9pQuo824FOfya0Bb1Mn/b/4PN94z6vRXDEfXjEUJcDPwvYAZ+JaX876zzXwWuTby0AMuBOimlRwjRBPgADYhLKddP2sAVijHiDcYwCzGihXg62UJuKojDUNR4sCUtiN5gLMOtcrKIxnV2tPfT7YvQ44vgDkQ4e0k9a2dXZrTzBKJYzQKX3UKZw8onT5vPvm4fs6oGs3vWlNqIajr+SJwyhxV3IIJJkHJjyeah1m/RFNxKpXUalzR8ntUV52IWVqaV21mUlWRmQZ2LZk+QDm84JQ5LbWYaKhw4uo0dSUfUTdA5I3XNrMSkZ7eYccjMHVubReR1B4129dH6w98T64UZH11G+akrMq8zZ4pD+0jEYREZSyO26qzXg5bDpEupU2UqVSgUikmh2xdmX9fY6ubt7higL+gYvuE4s/1wP9G4zolzq5hbU8LuTh9P7+zinVYvNouJi1YOnel7OILxfl7qvZ9t3iexm5yYxQygLqPWMRgibTiSlsN4RRWdX/48ZdZuMAlKwp3GvRyGp1FX9YksaX2ESv9+vGVLhuyzyrfP+GPJJSN6XyPliIlDIYQZ+ClwAdAGvCWEeExKuTPZRkr5/wH/X6L9ZcCXpJTpdvBzpJS5vlcKxRSnPxSlxGYmf9W34ckWcuPhVjpWcZikOyEOI3GdcEyf9IX/qwd6eXqnEYhe47LTH4qxvb0/VxwGY1SX2lLidXZ1CbOzdhyTFkJPIEqZw4onEKWyxJayculSY7//TRa5NmASZpaXn8HK8rNYWXEOZmE8Xs0m2DA/fxH7ZFbQpDhcVO/ChExNKs5Ib0ocWs2ChrR6gy5TlKCUzOx5if7S+VCzIKf/0IEOWm/bCLEwcy4IUnLp+3Pa5LiVWs3grARbGQS6jbIXBSi1WTCZQB9iwzM7CU26OEz+rcShQqFQTDxSSrY05c+kORJ0CR3eyXcp3dzcR02pjbk1JQghuPKEmdz53D7a+8NctnbG6GsZAu/1P8czXb8gqodYV/l+3lf7MVZ1Pg9ydEnzSra/S/3mHrovvZqSUj9oxlxbGuoganERsxobxt6yJUQtLuo9W4YVh5X+vUSc07DXFk5gMx4M+ykKIT4ipXxkuGOjYAOwX0p5MNHnQ8AVwM4C7a8Bfj/GeyoUU4L+UKyg9akYct1Kx5bxMzuGcSwkLYcA3lAUp805ROtMntzeyexqJytn5C8IWwx7On3MqHDwf85ehNkk+N3rzSnBmk5fIEr1MN9BdaJWoScQZW5NKW5/lJpSG7rU2DnwEq+6H8IdbeOqmd9kSdmpNFZelNPHqpkVlDnyx+bZE/UEXXYLpy2sYe2sSuyx/lRxe0da3OGMSueg66WuU2KKoUc9zOp5kRm9m+iwfQymr8vo3+xyYKsrY8byJmzrzgdz7iM/23LosJqgeiFMX2McCPVB/2Fw74OQN6OtSYDLZmEgXPj3F86qbRizliFNFoQeTytjcfyJwwmcWxUKhSIv+7v99BXIATDVcfsjNLkDXLhiWmpTt9Ru4WMnz2Fn+wAnF9iEHQopJRIdkzBjFQ5mOJdyXv0N1NnnYo+4Efro1lbmQ+2U/P4ZTDPnEjjz1IwEcqXhTgIJqyGAFGZ6Kk9gRu/L2KJeonmyegOYtCjlgSY8s85n6CrKY6eYmMP/W+SxkTITaE173ZY4loMQogS4GPhj2mEJPC2E2CKEuLHQTYQQNwohNgshNvf09IzDsBWKsdMfio3J2pedWTQ4VpfQcbIaguGykixoXigRTd4xRDVe3tfDK/tG7wwQjmm09gVZ0lCWElL1ZXbc/ghaWjFFKSWeQHTYDGZVJVYEg7GG7kAQU/lm7j54M3/tuA2TsPDBGV9jkWtDwT6mVxQWx0nLoRCCD6yZwezqEuyRQecIR7QvJRRnV6VZNWNB7FYzpaF246WllBn7HoDOd5FSMvDGbiOYfloVcz/iwlYOzD097xjSxaEQYDeboSStKK+zChpWwcorYdkHoG4ZlNaBxbBiljltROzV9FWsoKvmFKRpUIBKk4VYnhTcEWsVCFMq/rB0DLu9RzETNbcqFApFDpG4lqovfDSypaUPAZwwJ3PDcW5NKZesnp7j+jkc3eEmHmr9Jq+6/wDA0rLT+KfZ/0GdfS4AzsjoNIOprYeS+59Bryqj41/+hbLo4dQ5ocdwRnoIOjLdX7ur1gFiyMQ0FYFDmKSGr/aEUY1rJBSckYUQlwDvB2YKIe5MO1UOjEdhsnzfYqHI1suATVkupadLKduFEPXAM0KI3VLKl3I6lPJu4G6A9evXH6FS2wrFIOGYRiiqj8mNM1tYjtWtNDJO4lCXkh5fhBXTy3mnrT9VLqIYmtwBJNDaFyQc00YVj7m/248uYXF9WepYfZkdXRq7jvUJt8xAVCOq6VQNIw4tZhMVJUasYTAaJxyT9FmfpNxUyodmfoMlrlMQYug9Noe18Hl7nnOO9JTVUsce7SPmrGV6ZVp8RyyIw2LGHG5HExa2L/gMq9sfxvLmvXTsWcPAO93M+uKVlK2diWh7E2auA0f+NNnpCWnsFrMRt5guDtNx1Rn/ksSjWH1h2vZ6B4csoKH3dSCRfCbPhB2xVWLSoykhWXIcuZVOwtyqUCgUOWw/3D+mRHhHEl0aWdAXT3NR4Rw+S/ZQZMYVlrCs/H0AOfkRSkOdI+7b1NVHyX1PIksdBK+/BIfZT2moI3W+JNyNQBJwTs+4LmqrwOtaRJ13K4frz8yoj5ik0reXuMlOsHJo19PxYKjt2nZgM3A5sCXtuA/40jjcuw2YnfZ6VuKe+fgnslxKpZTtif92CyH+jOGmmiMOFYqpxkDIsKaNxVoXjo9vQprxshz2B2PENMn8Whc72gfoH4Hl8FCvkZpZl8bfy6fnFzNSSt5q6uNgr5+r1s3Cmmb52tftx24xZWQrqy8zRFW3b1AcJjOVDmc5jOtRnDWv02x/nc6B/wRMnFn2LU6YMbfoRDuOIepP5hPAjmhmemxHpJeq+sz3STSA3WrCFmon6GggbilhYOG1+G//HaGubuo2CFzT/dDyOmhRmH9mwTFYTAKzSaDp0hCrNhdYi0w0YLFRU27BJLwkDbOBktl4y71UDuzOLFuRRthWnVHr8DircTjRc6tCoVBk0B+MjTkJzWQSjmkc7AnQH4oSiml4AlEGwnEuXTNy19F0dg28zN877ySqhzmx6gOc77oAUWrE6wthlPIw6TGmud/AMQrLobmjF6xmAtdfgiwvxRUcdJC0WUxUa92Akak0m+7qE1na8hBVA3vwVGQmjkNKKv376HctzPDOmSgK3kFK+Q7wTkJ4BaQ0fJsSiWTGw931LWCxEGI+cBhDAH4su5EQogI4C/jntGOlgElK6Uv8fSHwvXEYk0Ix4XiT4nAsbqVZ1xoxg3LU5QCy3VRHS7fP8KuvL7NT4bTSFxqZOJxTXUJHf4h93b684tATiPKnrW0cTBTuXVzv4sS5xmQhpWRfl4+Fda6Mz6GuzJ4YWySjH6BgzGFMD7PN+xSve/6I3+VGhubSETDcXWeWNeQIQyG1vDt9VrPANMR3kl203qRFscZ8GceckV4qSrJ2SmNBHBYwhTrpqToBU6cbz++fRfebmfmxxZQ73oGtvzXaVi+AitkMhdVsQtM1nFYTlIxs8rWYTdS47Bmxpu6KVdijfalspNkYlsM0cWg/fiyHkzC3KhQKRQpdl7xxyI0+xX3nwjGNNw552NPpo8UTyBivzWxiVpWT5Q1lhTsogJQSTcawmGxU2hqY5VzJufWfYhYlzOx6gbC/CXflGqqnzaK3u4vpva9iiY9QSOsSTIJY42JiK+aBLde6WV9mp6S9g7jZQdSam1fB61pExFpBfd+WHHFYEu7AFvfjLVs8snGNkmLk59PA+UDyk3Imjp02lhtLKeNCiM8CT2GUsrhHSrlDCHFz4vzPE02vBJ6WUqZXfJwG/DmxQLMAD0opnxzLeBSKyaJ/AiyHYCSlKZT4ZDjGy3KYFGD1ZXaqSmx4i3QrDcc02r0hzllWj8NqYn937oN5c5OHx9/tQAi4onEGbxz0sGm/m3VzqhBC0OOP4A3FOGtpXcZ1NouJyhJrSrjCoDjM51bqj3u459DnCGheZjtXMVf7NK83VTFgKQd681obS4OH8ZfOyTk+XFmIbMuhPZpblNgR6c11o4kGsQZ7QMYIOGcgPBGEycTcb30M5/wGkJdCx7vQ+hosujD/zU0WSATb2yyCcCwxntLaIcecj/qyTHGIEHTWnIIg/+8qZimDNONk6fFlOUwyIXOrQqE4uonG9Zz6s2Ph9UNueo9QPeGR8Ke329jePsD0CgdnLK5jybQy6srsOK3mUW98d4cP8Vz3Lym31nPp9C8y3bGYj87+DgBVPZsAcETczOx6gQbbQio9rUTiI/ushD9EyX1PEr5gPdri2XmFoUkY86QW7DCS0eTzPBImuqvWMbv7BRwRN2H7YHhHlW8fEvC6Fk/KDmIxM7JDSplaqUkp/YkEMWNGSvkE8ETWsZ9nvb4XuDfr2EFg7XiMQaGYbJJJWkYbcyilzGvpC0W1UYvD8SxjUWq3UGK3UFliZU9ncamuk/GG82tLcVjNPPFeB95gNJXRtS8Y5dFth5lbU8pHTpxllJMQgj9tPcyh3gAL6lwpl5kl9bk7i9nipS8QpdxhSblqRrQgh8O7WVC6DpelmlUV57HItYE5JavYfrif12lhX7efCqc1070TQErKA4fyikPHMBN8tuXQkUccmvQYlfiBtDqJsSCyr4VQrxX/oumwqJ5Ft92ASLqwChPMaDT+FaJhNXRsAylTSWnsFnPheMMhqC+3syMrKEA3Z4pol8OCP5nVVAhi1vLEPU2jnviPciZsblUoFEcnB3v8dPSHOX3RyDfpYpqeMz9tP9xPU29wvIY3Yezt8rG9fYALVkzjnKX1Y+4vEPfycu/9bPM+hd1UypKyzD03W7SfklDmpOWKdKHZBT0j0YahCCW//Tsmd39eUZikutSGVUgswU481YUT2PVUnsDM7hep79tCS8Pgxm6lbx9+5yzilpJJEYfFbE0EhBCp/OhCiBOB0MQNSaE4tklaDjXdyB42UiJxHZnHPWSouMO4pg95r6TlMKbp7GzvR+a7QRH0+CLUJ9w4K0us+CJxYkXUyDjUG8BsEsypLkkVi0+3Hr6633DpTApDgLWzKymxmXn1gBGjt6/bR63LltcaWF/moMcXQU+8L08wSlWpjbDm55XeB/nZgU+yse17hDTDpfPc+k8xp2QVMBiX2DkwWKw+XctYtBDOSC9Cz3WhHS6pjtVswpLWWT5xCFCupR3XdXSfh/YHttD8bB2xLh27xTQoDIuldjGUGpOwLXGtw2oalTisddkZTt9Nr8gfx1h6HLmUZqHmVoVCkaLFHeSNQx4O94WIj7C2VDAa59Gth3l5Xw9ufyTV39GQnTSm6Tz2Tju1LjtnjEIUZ3PAv5lfHPwM73if5sSqy7h54S85seoDGW2qfLtzrnNYzZQ5RuDFEo5Set+TmHq8BD92Adrc3DjCJPXlDvB3IqRGMCsZTToxq4u+8mXUet9JrSmsMR+ucPuwNRDHk2I+hS8CjwghkhJ7OnD1hI1IoTiGCUU1omnZwsJR3bDWjIBCVr6hxOE7bf0c6PazsL6UZQ3lOaUDktlK32vrZ+PbbXzmjAXMry0d0biklHT7wqydVQlApdMQUv2hGLWuwb2uJ7d30lDhoDGtKP2h3gCzq5xYzSamldkpc1jY1+1n/bxqQlGNt5r6WDurMqM2pNVsYsO8al7c20PXQJhDvQHWz80f41ZfZieuS7yJwvfukJfKhtf42YF/ENGDLHadzGk1/4TTnGt1rEkTm8m/yxzWlMi3xv0gdSM9daJgfZJsy2A+7FYT8UiirmFCHDoivZQFW+mpOgG71YTV1w4mEwy0EWvZR9v/PEL4gJ+q9WYGZteP3AWppAZspVA5B/xd2CwCkwC7swysxdelTGI1m6gqteEewnWpodzB/m5/zsbGcZaMJp0vouZWhUIBtPUFefVAL1JCXEo6+sPMri7ekWBzUx8xTdLqCdHqCRklnAK59X0nG7c/QrMnyNzqEmpc+W1e/9jTjScQ5dPvm48l2zOnSKSURPUQdnMJdfZ5zCtp5Ky6T1Bjz423t8QDuIJtOccdVhPmAsleKpwW+kNpyaRjcUrufxpTey+hfzoPbdGsgmMrtVsos1ug27hnvmQ06XRXnUjNwE7W7L8rw7W0b5LiDaEIcSilfEsIsQxYilF+YreU8uisoKlQjAIpJTFNjngBHopq9IdiNKRZTLyhzMVzKKZRwchcQQvFB4Zi+bPg9wWi7O3yISXs6fSzr8vPvNpSTphTmRKmyT57EjuOezoHRiwOfZE44ZieSgBTmUii4g0OisNoXOeV/T1YzSYW1btw2S1EEvGGZy0xYgWFECyqc7Gny4cujUD6qKbzvsW5O4onL6jhpX09PLKllZgmWTLNldMGBpPSdPWHKHdY8Me8YH2KJaWncHrN1UxzLCz4vuxWM6U2M4GolhKHlSVZ4hAjRXW2OCymHIfdYiIQ0bDE/Jg04/Of0fMKdf3v4nUtwuGshv5W6G8ldKCDtjv+jBYIM/OMfiwnnwhCFCVCM6hMTJgVs6DtLaxmE3arGVE6cqthEqOeZGFxWO6wUmIzE4hk/n6PV8uhmlsVCgUYmUQ37e/NSMDS6gkWLQ5bPUHa+jKdDtITsE02nkCUl/b2sK/bR18ijMZuMfGxk+dklJkCw9vopb29NM6uZGFd/vl7OLrDh3i2+24kko/N/gHl1lo+NOsbBdtXDewBmWmZtVkEZiEwW8zYLIJoPHMXc0aVE1/YN/gdmU3o9ZVo6xewyvwXOt0n01lzSt77TStPiGLPQXSznbAt/zxrEkZem4HSeTQ1XExZsAVHtA9H1E3A0UDIPnZ322IZdkWRiIG4FfiClPI9YJ4Q4gPDXKZQHDOEY3oqgclI8IVj7O4cyDjWn5W9czSJYAplOS1kOXyzyZNhrdElHOwJ8Ld3O2hxB5FSEknEMPYmxOHuTl++roZ0delJJaMxxHDSypeelOawN4QuDdfYZ3d2AdDsCaJLmF87ODEsqncRjGq0eYK8dsDN4npX3mLyFU4rq2ZW0O4NYzaJjD7SKXWGsNf/jU0DP8YbjKFH6zjLeTsfmvn1IYVhkuSOZ3Xivw6rOSXIrHEjV1ZJOLcmUlHiMNGmJGKkuEZKKgKHjPfnP4AzrQZg+FAnwmxi3r9eQPnMAKYqQ+SN2HJYaRT5xVkJjnKsZpMx1pLRu/Qky4QUotSe32XneLUcqrlVoVAAbG/vJ3tqPewNoRWRXjQa19nS3DdBIxs57d4QP3/xAFtb+2god3DZ2hncdOYCqkps/PbVJt46ZHjHaLpkb5ePP2xuxWoRXLJqaGtaPgJxL3/v/DH3NH2e7kgTy8reR+Fy6QZmLURZoCnneHrJKZc9c8PebjVRbrcac3FcQwwEwGQifPn7mNbQhj3Wz+yuZ3GGu3P6tZoFNaV2o6RUxzZi9avyJ6MBplcm1jhC0FWzgf2zP8z2hZ9h87Jb2b7gMwWvmwiKmZV/g1GL6dTE6zbgEeDxiRqUQjGVCMc0ev2RDAtgMQyE47R7wwyEY5Q7Bq1o6YymnEWhshP5xOH+bl9Ba044pvPK/l6mVzhSu2FufxSBsevoCUQzMnNuP9zPw2+1ctHKaZy+qDannEP3wGAZC4BypwXBYOkOMHY4AdbOquCtJg+nLKjhUG8AkyCjNuHCRNzho9va8UXifGRxZgbSdE5fWMu7bf3MqynJEUm+WC+ve/7ENu/fsVXHCMdOpjdg7LDOKCt+MqoutdHiCaYsh3aLiRKbmUhcT6W8tsZ8mONBNMvg+yjGopeclJL1kBxRN7a4Ic4r/fuJm08l3NqDY3Ydlec1Un76Csw9bwNgqZ4LvWAzj8D6ZnNllquomIM94DXGOop4wyR1LnuqTlQ2TpsJi9mUmHQzd7RLbMen5RA1tyoUxz39oRgtntyEMTFN0jkQZmbl0G7+77Z5x1zneLw42Ovnd68147Ca+ZezF2VsGN545gIeequFP287zI6Ofg73hQhENRxWE1c0zhxxMr22wHs8fPi7xPUo66su4/Taa/KGhSSxxnxU+A9QFmhCyNzPKz2zeJnDkmEQqEpsdDvNJvRHXsDc3ov/s1dhNYWZ5tlMX9kSXME2Fhx+jB0LPmUkhEtQ4bQZ8fgd70I8gjZjAxTI09dQ4cATiOauCydRFCYpZrt5oZTy/wExACllCMMFRqE46hhNopVwXMM9CsuhP2K4ee5Js8KNi+WwYMxhPGOnMRzT2NY6fDB6R7/xpNKlpNcfYVmijtCeLKvny/t6kEie2N7Jw5tbM2InwRCUDqspZR2ymIy/0y2HSYF12doZqaykh3oDzKrKFHblDisN5Q46B8JMr3CwsK6wi+vs6hLOWlLHmVkCcp/vde46+Gm29P2V5eVnUtX3TUzua/AGjc+vqkCNw3zUlRkJV6rTxGHSomeND07sKetfguIshybMWhhnouBueWJXc6BkDi7PIYJ3PUbTd+4n5vEhhMDstIO3FSwOLK46rGZhFK8vlsrZOa+tZhNO6+gylSaxWUxUZddjTJAsVaEshxmMem4VQlwshNgjhNgvhPhanvMVQoi/CiHeEULsEEJ8cnyHrlAoxoMd7f15N9TASCgzFP2hGPvylH06EuzqGODeTU2UO6zcdOaCHE8Sh9XMx0+ZxykLamj1hFhQ5+KfT57D/71keSpPwXBIKQnEDSvpQt3GMtep3DD/Z5w/7cYhheG03teZ0/EkFb59GfV1s8eXJFuoVpfakLoODz2PdWcT0VNXgd3KjN5XEFKjueFCmqa/H1e4nRm9mzKuTXn+tL1pzK81C/Le32wSWIQYUZzpRFLMrBwVQjhJ2GqFEAvJ3vpVKI4CwjGNSEzPLSg+7HV6KvvXSPCFjYfQoZ4Aa2ZVYLeYc8ThaEpIRArFHEZ1/rC5lRKb4b4X02SOgBuKgVCMuC5Z0lBGjz/C7k4fpy403AzbvSFa+0K8f/V0NE3n6Z1ddA2E+eeT56ZcLrt9kYT1aHB9W1liS8UcSClp9QRZWO+ixGbhvOX1PP5uB0Aq3jCdRfUuOgfCnLE410qZzUUrDStgX7SDiB6gwbGImc7lrK24iJOrP0SlrYHHutvZ6u7DE4hiMQlcI8hKdtqCGhbVuVITiM1iSokaa1qx3JJwF77SeanXjiJEm91iojR4OGVyKw8cImItp1NfifmpLUSDLTR84gKs1WmTX3+rkUxGCOxW88jcSiuyxKFrGsJix1VmA9vYJqa6MgeeQO7km/ysXfbcz/x4jTlklHOrEMIM/BS4AMPa+JYQ4jEp5c60Zv8C7JRSXiaEqAP2CCEekFJO/WJnCsVxgi8co3kIAXjYG0LXJaYCqaAP9OQm+DoSdA2E+f2bLTRUOLj+1HmU5HnOgyGALl87g8vXzsh7fsh7hA/wbPcv8cXcfGbBzyiP+fhI1ccI2gtn/gSwxvpTXjlDkT5Xl9iMuoqaLrFbTbisZjp+9STRt/YQvuAkoqetwhbtp77vbXoqG4nYqonYqnEPrGBmz4v0lS0l5DDiA502MwQ90LsPllyEtYCXj8VsfMdVTmtu8psjQDErim8DTwKzhRAPAM8B/zaho1IoJgBfOE5fkUXZ0wnHNMIxPWUJLJZkTbe4LjnQHcAfiRPXMp/ko3ErTVoO2/qCfPevOzIsc1JCIKLR2R8ZMjlIPpJFcmtddpZOK+NgbyBV/uKNQx6sZsGJc6o4a2k91582j4FQnLtePECz24i5M8pYZO4Wpidu6Q/F8EXiqZ2xk+fXpBLV5Et+c/L8as5cXMvqmZXDjt0TPczjHbfzi4M38kzX3QCUWCq4qOEWKm2GcKwvsxOJ6zS5A1SV2jCNwFXDbjVn7OjZEm6lJj2GSR/8nLNjDorJROuwmilLTl5SUh5ooqd7BvEHd6DFBHP/eR5V5zUOXqDFYaA9JfKcVnOqTuGwmG1QljWZCgEVsyipmFZcH0OQdCnOJikKsy2HJmGM/zhltHPrBmC/lPJgQuw9BFyR1UYCZcLYVXEBHuDIrjYUCkUGO9oHhhR30bhOly+/D6KuS5p6AxM0skw0XfJum5d7XjnEy/t6cs5t3NKGzWLi46fMLSgMR0sg3scTHXdyT9MX6Ik0s6H6gwhMOKJ9lIY6hr2+PNBc1H3SYw4Fg3NWVYkN9+Nv0P/Se1RfeRrRM40S6zN6XwbgcN2Zqeuapl+CZnKw8PCjqXWB02qGtrcACbNOwmzKP1enl7SaXT2yZIATQcFvUQhxupRyE/AS8CHgFIzP7AtSyt5JGp9CMW74wjEGwiNfH0US1jePP5rX8lH4foP32tfty+tSNxq30mTMYbM7SCSu09EfzijxMFqSaa9rXXaWTS9n0wE3B7oDLKgr5Z1WL2tmVaZcJBZPK+OWsxdy76tN/PqVQ3xgzQz8kTj15ZnioNJpY8fhAXQpU3EVc6oMkWU2CT7YOIPnd3czryb3YVjjsnPxqqF3Bd2RVja5H2LnwEuYhYX1VZdxcvVVedsmhUtbX4il0wq7oBSDzWy4laZbDQHMWhhbtJ+orQIoznLoJIIjajxSSyJdWLUQgU4Hep2NhjP7KHG0ZF7gawepGZZDDBfcomVu+QyjJEY2FbMhNPakBvlqTMLgRJv9/4/TZh7WKnysMQ5z60wgfSu8DTg5q81PgMeAdqAMuFpKObLCaQqFYsIIROJFibsWdzBvMrbD3lDB/APjRSSu8doBN68fdDMQjuOwmtjf48ckBKcn6hG+uLebw94Q12yYM+K4weHoiTRzX/O/EtejbKj6IKfX/hMOswuhx7HFBgq6iKaQkrJixWHWJmWZw0J/yCh9VXLaXMz9dVQu7SLe5yOMnbq+bXRXn5ia6wHillIOzrycJS0Ps6j1jxycdzUOM4ZLac1iKKnBBCmrZDqWtHm51Gam1mVLbdgfCYZa6d4JnAi8JqVcB/xtcoakUEwMvnA8w8pWLEnrXm8gwpya4tzuQlGNeNr//IGIxo723Pi/scQcJjOLjiaTaj56fRGsZkGZw0Kp3cjGubtzgP5wjKimc/L8zBqCNS47N5+1kN+93syj2w4DgyUjklSWWNGkxBeO09YXwmISGYl9FtS5WDDK9NUATcF32Ot7nQ3VH2RD9ZW4LPnrHEJmNs3qAiKmWOxWMyXaYDKadErCnURtFUZh+iKEjzPQZph8QxFcbXsACH7gDKLmMvTQm9D6LER8YE8IWm9CLCYsh+XOrMe4xQ7xAt6JCUGZQ/lMMI99YnfZLdgsphx35qQotJgNi2sygcJxGm841rk1348q2/5wEbANOBdYCDwjhHhZSpkRSCyEuBG4EWDOnAK/DYVCMe7s7hygiGSktPaFaIxrOV4ohybYahiOadz7ahMtniCL61188IQaFtW7ePitVv72Xgc2i4mZlU6e393N2lkVrJ5ZMXynRSClxBvrpMo2nVrbbBorLqax6hJqbDNTbewxL0gdSzyANTZAzFqety9nuAuzViD7Sxp2q4lsz12X3Uzp5vcoXeLBdHgTVTUSWs3M1Iz1liYstNe+L6cvb9kSDk1/Pws6/saC9seh7H0QdMOSi1Nt8olDqzlzAPXljikrDmNCiN8As4QQd2aflFJ+fuKGpVCMP75wPCfmrxiSrpUjcdP0RXLvky8WK65JYpqOtUi3wLimpxbeySQ5o3GVzYc7EKWm1I5JCExCsLjeqDXY4gkys9LJrKpcYVxqt/Dp983nkc2t7O705exwVqVqHUZT/ZgLxE8UQ2f4AJt6f8+C0hM5oeoS1lZcyPKyMyix5J+YTMLYEQxGNUpt5pQwGas4tJlNlFgtqTIW6ZSEu/CWLy06SYzd14ypqw/n758hFvcRvKyGqKMKAN21zBCHPXtg1noI98O+Z6C0DpxVqbEMDswFyy6FA89DINP9h/oVUDU//yAsNkMgjgNVJVa6BjLFaXp8Z5nDkhKHpcdnptKxzq1tQHrg6CwMC2E6nwT+WxoZuPYLIQ4By4A3s+51N3A3wPr166dA9JJCcewjpaSpd+hkM0micZ0tTX2ctmiwzFA4URt4tCSFSaG5OBzT+M2mQxz2hvjYhjmsShN+V580m/tfb+bRrYcTG8kWLhtFDGE+OsMHeK7rl3RHDnHTwl9SYi7nvGk35LSzRwe9XErCnfQXEIflwWJdSjPnaiklofv+hPnlFvrX91N19jpYegk4Kmnv9eDu6UI3WYlZ83sg9VSfiC3uZ1bPi/D2AWPDtmFN6rzFJMhetVnMwkhYE3QDRhI3kwn0I+TvMZQ4/ABwPsbO45bJGY5CMXH4wjECEY1oXB9RAo+k60ZfIDpkcHjmvYp3Xw3FtKLEYYs7yLY2b+p10nLYN16WQ3+EhjTr2rKGcra3D+ALx/nQCYWFg9Vs4poNcwjH9IyafAAVCXdXdyBKuzfEKQtGlw2zPbSHTb3/P3tvHR7Xmabp3weKWSVmW2ZMTLHD1KHmJE1pmuaZ6eGd3lmY/S3vzs7M7s7sUDNOwzRDOt0dZschJ2aSLUsWq6RirnN+f5yqUrGqBIbk3NeVy1LVqVNHslPfeb73fZ/ne5yJvIhJtNFn0/r+ZdGILJYLvat73fQ2WbEaJcKJNL94fRxBEGhxmDjviy5JHIrCfK5gaVspaLuVpuQc5nqiMpJREk89i+2Lv0I1GujYO0fIsTH/tNzUowm+qePQcRW8/FVIx+Caz1S2t25eq5nKrLsLhp6BuSFtzrDvWmiqIgxzLFN7p8dmLBKHklg8V2g3yUxmfVeWez7lCmGpa+tLwFpBEFYBo8D7gQdKjhkGbgOeEQShDVgPnF30Fevo6CwbE8F4flylHoZ8UXqaovm593MzkbqqjpXIKCr/77HT+CIJmmxGvDYTLQ4THS4zHW4LDpPMN/YPMe6P88CePjZ1FgsvWRR5YE8f39g/xLmZCB/d17fkDpBwepanpr/JocCjWCQHN7V8FLNYfe6uUBzaYhMEHOvKjhGVFLZo6Z5ZZQpbSlVFYfoL/8zccxO4t4i4P/a74Jq//7HanYyEF14rR1tuxCPFsU0cgJ69mkDMIle4h5QlGTx9eXEoCppAbORecjmp9Tf6OVVV/0wQhF5VVb9x0a5IR2eFyP1P5o8ly4xTapGrHKYVFX+2B73e96qHeDKTz0GsxGwkyctDs0UtBqmMQiDrAjoXbbwaWkpGUZmNJNncOb9DuK7dgYDWcrFtAatpQRDKhCFozlug2VynFXVRNs2PTX6ZF+d+gll0cGPzh9npeTtmqfbAdrvTjC0rPArbcVodZs77olVn4+ohJ+SNsohZqbz72xQ4iqG9diVOTaeZ+h//mdnv/QKltxXesQnHzHeYsM2LOIvRAC0bNHF45PvgPw87P6bNDpYiCJo4BJBkGLgFJo5oraTmyjurK0FpRIjVKBe11xZWEd+kGYdLWltVVU0LgvB7wG8ACfiqqqpHBUH47ezznwf+K/B1QRAOo7Wh/pnuFaCjc3mwUERFJV48N0uLw4TZIC2ppfTYeJDpcIKre9ykMgoz4SSD0+GiMRhJFHjgml42dlReN4yyyG9d2890KEHnAjmMlRDUDKqgffaHUj6+eO4zpJUUe5rexXVeba6wFubk7PzXiRkEJY0qFssZe3QEQa3vPix3j6AqGab/6Sv49vtxb7fQ/se/jSAX35vVvWYJAslN92Jr6YW2rUVPSRWKAaLJBrbWosccZsNlKQ7vEQThz9F2Jf/yIl2Pjs6KEE2m8x9+gWiqIXFYGDfhCyfqEofhBiuHtXhpaLaspXU2kkRFa8ubjSRRVXVJxh7+aBJFJe8eClqFZ2efhxaHqbGohAJMBgmLQcpnPfbWKQ6Ho4dpNvVhlZyssu/AKrvY4X4rJqm+1xcKVaOszRMoKqxqtnJ0LEDTEgx8CttFbcSoJM2tsXGMaR/QXOFZIJOCuWHihw7huf1qJm7egXdKy0cK2voAbQbBIIrQuhFGX4aRF2HtHdCxvfI5nd1gLBHN7Vsa/OmWTunvtjQypHAj5E0qDpe8tqqq+hDwUMljny/4egy4YykXqaOjs/woisrIXOMtoYm0wktDs2zqcOJfwobwc2dmaLIZuW9nd96xO6NoGcdj/hiTwQTr2x0VHcQLMUjiooQhgCk2xXkxRpt5NQ6Dl+u8H2CdYy9NxoVHGwQlhSE1nx0tqBksiWmilmLzunqNaCBrHKdkSD/7Dfyv+HBf7aH9jz6FUCF2wiiJGCSBVGbh0q3FaIC+68oer1Q5lMxOsDVrm7xZC9tqcVtyve7kS6CWOPw1MAPYBEEoHGIXAFVV1Yu3Fa2js0QKd1/8DcwdJtMKmYLuj5lwkrUFjv+JdAaDKJa1muYyDuuhljiMJNIVZx1zj61ptfP6hQCRZKYhJ9VS5mMsim/s793Rvehz5nBbDYwH4jjNMi5L9QqpqqoMRV/juZnvMRI7wo3NH+a65vez2raD1bYddb9fbs6wEKMsEk8pbO92s63b3VCMRSn5GT9FwUqMcpshDc/cEbRuvgL8w8SefgiDQ0V2mOn503sRjTIzYwGckXNETG2kZW1RzgvclvUgStCysWiovYxc1fAS47TIyKKQ34wp/XdZ+L3tzWlIo6+tOjpvUsaD8YbyhwsZmY0RXEL+3bAvwvBslLdt6yhaAyVRoM1pps1Z/6b5YpmIn+Gp8b9hKDXMp1b/E03GLvZ6KzuMV8Jc0FKawxYbLxKHhlQAc6Jyo4Q9Okzc2ERanq9OGiUB9ciPMAQPsep370TecRdCjfEhq1Fe0L9CEoWyWcbC50qRLXbNFM7szjuHO6rc0xVu4q8UVeWnqqqfU1XVBfxSVVVnwX8OffHSudIIFvyP3MiuWzxdLNxycQ+g7QA+c2qGqVC5M2SogUzEWlmHufiHUnLXsaZVG4he6txhbn7RuwIfOu6sIKzVUjoYfplvDf8p3xv5c/ypcd7S+hn2NL17Ue9Xqb011zYiZM12lkK+ipoMa5W9ateRmILgfA6TOvY6s//4lwz9279j6rtPACBmxZFRjeOIjhC09c+/PidwjXa48c9g52+BUOX9DNbycPtLhCAIuKzzmwCl4rAw0sVqevNVDvW1VUfnzUsuF3ixLMZUL8ezgz7MBpGdfZ4lXcNiCKdn+eX43/C1oT9iKj3BHe2/g9tQx1x+CaYK4tAan9C+UBXcwZOsnnmyogCzRUfZfO7rbB38IvZstrCASvhr32X6wcOoq27BsOvumsIQyI+s1KJ0g7qQSpVDgzlrbmNrKTqutLtGloQldT7VS62cww2qqp5QVfWdgiCYVFVNFDy3V1XVF1b86nR0lolCsdZInEW8pKoXjKXzhjYvnPUxFUow6o8VxTPEkpmysPtaLEYczoSTWAwSXdm2jrloclHzfDl8kQRmg7gi7pG5DMZaLaUH/b8ilPJxZ9vvss31loomM/VS6UPZtMi22ErkZxgTIYyyiJyOYo2PE7QPFB0niyKMHQR7G5njjzHxV/9I8IUT2K8eoO2DtxQd2zLxLIKaZtpzVf6xIpFrL55FKMO7pnJ+4SXCYzXmq9ul4lCWRCxGkVRaLbNnfzOgr606Om8OSg3sMorK6CJaSpeDuUiSo6MBbljbfNE/d5NKjC+d/V2SSox9nnfwgYSDcded1Tc7a2DKzhsWdF8ipyM4w2dxhU5jTAVx2010epwMToaJ5O6vVJW10w+jmhyAgY1D3+Bc211kDswwu38C99YW2Pi2uq6hnnEISy1xKFUQh5asOLS3wsyp/OM5d++opR1rfBK3xXhRlvpab/Gdgq/3lzz3jytwLTo6K0Zh5TCVUYnUWdlLVAiZnY0keX3Ez1B2qHy0xFK6UoxFLaq1lVZrKQVt9rHZbsRj0yo0S68cJmm2m1YkkNydrSL1ZKMwVFXhRPA5vjb0h8wmtXzEu9t/n98e+BI7PG9dkjCEyh/c9cxMypLANaub2NjhoM1pojV4BEtsovq5EiGMskDnzDNsOP9t5JJYC4MkQHiS5GNfYej3/xPBAydpee+NdP/xvUi2gvadeBDX5Av4XFuJmed7ls2WyjbZFblMWkpzNNnmK4eVdlntJsObsmqYRV9bdXTeBDx6fJLxwPz9wZg/Vtes2nIQTqSL2lf3n/UhCCzaMbxRVFVlOHoEAKNo4fa2T/Op1f/EHZ73YkXGkA4tcAbKsgdhvq3UWTKP1zL7CsaU1qVvMohYZIlNnS5anSaMssBWTmMKjyBseDvCDX9C0LYKx2+eIbP/Au5NIu1/8tsIYn1rUj1rV6UOphxSBVFstJZXDkFbKxPGJsZbbmC4/U6cPZtAWPm1s1ZtVKjydaXvdXQua0odn/yxVF2tAaWVQ4DXRvxFwfPheJpgPJU32mjUXaqaOKxWNQQtGmJ1sw2TrGX3zS7RsdQXTtRtFtMomzqczEaSdHqMHAs+zfO+7zGdOI/H0Ekk7afJ2IVNdtd9PptJoslmJBirnFtZSRzWUzm8dsA7n+UYniLqH+fQ2GlGW24kYZpfUE2F4lASMUaGEABHdIQ554b8cXJ2e0+Uk4hmI73/9r3YNvWVv/GZRxCUDBdabso/pMhWbP074MKB2hctm7Wq4UV0I60Hd0HbS6VZWIdZRk6+aZcRfW3V0XmDMxmMMxNO8uTJaa7udbOh3VlzTV8qGUXlyGiAszNhzs1EmAknkQSBXq+VgRY7Lw3NsqXLVfTZvFKMx07z6NQXuRA7xod6/xc91i1sdd0KgJzdcDWmgqQMlfOJQduEvXFdM48dn8pXCMVMIr8J22QzEoqnK0Z6mLOVUVGAVV4bilNCfPIhzbm7excGQSQwsono8RiOjSLtf/IZBEP9vxeLLFUMsi86ppI4FGWweJBjxREbkiggmrIzkBa3Fj+V0e4xHWaZUDp732B24t20CVj5DYZad8dqla8rfa+jc9miKOWVQn80mW/JrEWlLKLZClW60bkYzg5NHDbiVArV20qrLSTJtEIglsKbNY9pshmZa6BVtpRURsEfTbGjd/nmDQsNSbx2E2/f3s7Xh/6IycRZvMYe3t7xp2xy3ojYwA5Yk83ALRta8y0xrw7PVRSHFdtKa7R4AGzrds0Lw0wahp7BKAsISprOmee40HozqWzQ7vzMYQijmsAanwTAER2eF4epNOFfv4Tlnt3IDiv9//nDlauy0Vk4/zzxjl0kTE35hwVPL3LzAIy+BGoF84K2LeDpn3c3u8xwWwwIguZoV6lqazfJb2YVpK+tOjpvcE5NapUxVYVXz/uZi6RWtKX0ocPj7D+rzRT2e23s6msimkxzeirMo8e1Ner6NVXcs5eJUMrHUzPf5HDgUaySi7vbf58uy8aiY6SM9jswpYJUm740SAK3rG/BazfhshjyPhGFZjR2kwGjLFXcwDcailcXcfBRSARh18fzrazugQ5iN2xFev/NCObGXVd7myzMRVOEE+mKY0QV20o9fSCZkGbHix42SKLmLZDD1gJBravKZJBRPKsgDV0eS7ZNeeVXz1risFsQhP+XvYrc12S/X9hvVkfnMiGcLN9dCtRZaVsoZiLHmD+WzwRqtHKYyqhkFLVogLpWS2lOnObMYzxWI2P++hcdXzjBM2dmuH1jG3aTnI/FWE4zmlUtNk5O+hkMvcJaxzWIgsRm583sM7yXjc7rqN3RXpkWh6loVqKak1elD+ValcPeJitbugp2MMcOQjyILAja30kmQef0M4y23kJathZVDk2BswhAWjThiA4DIPgCWP/lcXzjPiw9LTiuHqjernv61yAIJFbdTqHtqaVtQAvNdXWDf7j4NbZm6Nld9ee5HJAlEafZQDXHbafZkN8NfhOir606Om9gosl0mRBcSjbhQpyeDLH/rI+9q71lTqR3obWYBmOpRUdP1IOiZvjW+T8lnJllb9N9XOt9f8XoKTkTB8CYrOzzLYsCN2WFIWjrfk4c5uYNJVHLVTYbxIri0Fw4UxmZgbNPQPduVHcfiZFpzD0tuK7dhPWaDcQX6Rzb6jDnI9FCiTQnJ0L5SmJVp1LvGu3eomTmUDSYtWziHLbmvDjE3UOT6iDki+ZHcy4GtcTh5wq+frnkudLvdXQuWyqJtXrjLCrNHFZiOpTIG9WEG5w5BE2EFrbfna8RkptzFm22zYvDY2NBFFVd0IlTUVW+//III3MxJgJxPnH9Kny589mXp90ko6Y4HPw1Xxv8Kv7UJB/t+z90WtZzTdauusVhQpYExv3xstc22YwVK7NQHq5erS3YWiEeoVAcSpkYGcmSPYfE3tXzFTtCkzB1NP+tURaJJTPI6Sjdk48x0bwPo5ydCUiEkWcHyQgy056rafO9iOHwKcw/2w+iSPe/uhfH1cUmNYC2lZwIQmAERl6CVTci2ZogoM1LZCQz7pZshEjT6nJx2HbxswsXg8dmQKnyv4/dLJOq9uQbH31t1dF5g1ApY/jMVLhiu+NKEE2m+dGrF2hxmLh7S3vFewC7SV5S1FU1VFXlTPhFBuy7EAWJO9s/S5OxC4+xo+prJCUrDtPBsucEAW5Y11yUQ91iN3F6MgyAOekDtHVbIJtPWHp+UZiPmwIYfAwQUde9lYmvPUzg6SOs+u8fxdTVjEEStardEnGYZPq9Ngans9dZqWpotIOjAwQRueTvSDCVZEoWGtB519KSMDHqj62ouC+l6r8WVVW/cdGuQkdnBamUORiMpcqcxCpRb+VQUWEiEKfXayXYYOUQtNbSwg/vheYNgXxbqcdmIKOqBGOpBecJnj8zw8hcjB29Hl4dnuMnB0dpz2YbLTU7J62keD3wGw7M/pBAappe2wbe0vbbdJjXFR1nM0ls63bz0KHxfOspaK0kN65r5pFjk0QS5b93r634+qqJw4pRFgUf1i2zB5louRYAl8UwHyirqjD8PIUlLaMkEkO7FikTp2vqKcx+I1jXaDMBvjNEbT0EbKswPHMEy6GnSXe3wIfegmNLSUZkJgmvfgN8ZyCdNag0WGHN7Zp5TZaIpYPm3OLo7i2aP8Dk0NpJrwA8VmPFtmzQ5igWm/V1paOvrTo6bxz2D/rY2OHEY9PWXkVR8yJhpVFVlZ++NkY4kebD+/qXRejUy1jsFI9OfZHR2HHe2flnbHLeyIB914Kvk7NtpYZ0GEHNoBaMlmzvdtPhKhZALQ5t3TcnZrBm5xXtJm2Ep5LratEYQyYJY6+itm9n/J/3E3j6MN6378XYucymPM5OmhkjEDPmneTLaFqtqV+zuyzEXjSVmM/lTGkMVnB20RJL0+22VIznWCnelAnEOm8uKlUOFRWC8YXFVCJdnzgEzbW01WlqKMYiR27uMJbMcGIiWLV6BlpbqM0k53encpk3c9HaP89MOMHDxybZ2O7gvh1dNNmMPHp8EqtRKjrfYlFI88zMt+m29fGXN/031NhazvvK211zu5ibu5y8PjLfWrKrvwmrUabZbiKSKBbHsiTgtFQPUy+kVlupnI5ii41iTswQNzUXHzt7FmL+oteVzcupCpbxFyAxBskIhMaIdd5K2NpDX2ecgNxH8O1vw+moILSPPwiTR6F3n7aDaG/VsgmNNuQCQaq4eueFryhpcwozp7Xv2zZfljOGldAMAypX0Q2SmHfa1dHR0bkSSWcUhmejjAXi+Rm5kbkoseTF2fh6/YKfI6MB7tjUVpeHwnIQSs3w5PQ3ORJ8DKvk5u72P2CD47q6Xy9l20pRVYypIAmjlrnY02RhU2e5sZrNJGM1iHgnDuYfs2fdQit5CRSNkIwfQk0mGHtKIvjKYZrvvY7md1+7vK7sBgusugkOf5++ZhvhRKayGY0320VkMCPI5iJDG8lsLz5WNmkmc65eEEXcVgMDrSXHrDC6ONR5w1PtBtW/gJgCiNfZVgra3OFAq23hAyswE0kwGYpzdjpMZoG3nAknabbNX3dux3IukmRVc+X3V1SVH796AVkSeOdVXQiCNvA9E07w2oifvkU4lSaVGAfnHuJ0+EUe6P0fGEULH+//O969bSNtTguHLviByuIQYGO7k/O+KP5oip4mS/7aWxymsrZaj9VY9oEuZQNiowWGPrIoVDRAyS0YuQBdd/AUEy3N8y2oqgrjr5e9rtK5ZFHU5gF8gwSGLASH4mTeakZp89LcFWNGlsp3cKdPwtDT0H8jbLm3/JyCgChCGgP2lt7iJ5sGNHEom8F7eUVW1MJjNdacK3wzZhzq6OhcHtTTObQQE8E4iqqZxD1+Yoqb1rdwavLiVA0T6QwPHhqnt8nKDWtbFn7BMqCqKj8c/a9MJ4bY1/Qe9nnfW3GusBa5mUMAYypAwujBaZFrRmz0KsOkkv7897l7iEpeAkWtpiMHCIy3EHxlhJb33EDzO/c1dK110bkDDGawepHDU6xusZW7mNqaNRfSHBY3sjRRXRwC2FrzglIQBNqc5vJjVpAFxaEgCNepqvrcQo/p6FyuVDOIWWjuUFHUhlrfEmmFoZnFWVWfGF848yeHL5xgbdt8G4LbYkAAZqs4lmYUlWdOTzPki3Lfjm6cFq1iIwgC917dRSKt0O+t/wM+kYnyytwveHHup8QyQfqtVxPLhLDJbtptbbQ5c/N8lT9e7Nl8IlEU2N3fxLNnptndPz/3V6m9tclWWcTbTHKROKyWLZQTIjlxaIuPIafCWLK7lsyehXj5gLyxtP1D0ASoEk8y8a3nCBzyIK6Ka+6k1h6aA4dAVYraRElG4fXvaJXCGiG7BkkkaOyg21GyA+xoB6NNG2aXrpz9PKMsLtsc6xsRfW3V0bl0zEWTSKKwpGiH8cC80EllVJ48MV00KrGSHDg7SzSZ4Z6tHSvabqiqKidCz7LathOTZOXOts9ikRw15wqhOKS+EKlIHGrmLDesbaneEpuK0xE+Tm7y3mSYnxM0y1LZ+xhzm45RH/hO47r5LuRrN2Df2l/nT9wAFs98xrCtFcJTlY3yvGuKvze7kUWB7HAJsrlCpnHLBrA2lT9+kajnTuPvgB11PKajc0k4Ph7EapTo85ZXzdIZpeL8Gmhzh7WIN9BSmmNoBR3JABKpDKFEuuimW5ZEnBYDcyWtqJPBOK+en+PgiJ9wIs36Ngc7et1Fx8iSyIf3Vsjeq8J04jz/fP5zxJUIA7ZdXNv8froLrKp7muaFTbW2z8LHWxwm7tjUXtTS6rEakCWhqD3XW0Uc2k0y06EEhlSQlMFZudcfTaiIApizbmeoKp7QKcyG/mzV8LWqrytEEgXiw1OM/v0vSI4F8e4ykv7o/fj9cYK2XtrmXsYan0QWV8+/6MgPIRGCXZ/U5gerIIsCYUtXfsYijyBoi0vrpqqvvVwpna3QKUJfW3V0LhEz4SSCwJLEYalL+MUShsm0wjNnZljTal+xfGKAsdjJ7FzhCW5r/RR7mt5Fp2Xdgq8zySJ9XmtZFVXMJBHU+fsqYypAq0OLq6jK6Ms4jfO/18L7B1HQ1uhC40CzLKIkUkz8/Y9o7pEx9l6D3eKZP1811VqK0a61jEamqx/Ts2d+zMPeApMVjhFE8KwqfszsKhL0BksFcWi/ONXgalQVh4Ig7AOuBVoEQfiTgqecgN4PpHPZMBtJcnA4yqg/xq6+pqIb+nCiujlMsEq7aY5GWkpzrPTi4CuJscjhsRqKsg6Pjwf51gvnEQVY3+5kZ6+H9e2ORfXaRzNBZhLD9Fq34DV2s9F5E9tdd9BhKW9x7ClYqCpVDiWx3E209DhBEGixm4p2ZT01xCGANT5BFLAa26v+HEZZzFcOARyR81jFdLZqWO6cBuWVQymjMPzXPwBUem/2YbvpLfiyP0/YqrWDOqLDGKTsfMGFl2HsVVh/D7h7ql4bgCQbSFo7cFdaKDuuAlEXWm8E9LVVR+fSMxNOoKqwrq3CjXkdBGKpqhvPK81LQ7NEEmluXd+68MGLIJia4anpb3Ak+Dg2yc097X/IVtdtdb9+U6cTp8VQJg4lpVhMG1NBLNWEYSIM/vMwcxqrcT50Pi8OR18Bdy8m2VQkDg2pDCP/5ydET8xha+/BWCgMAZrXw8zJ2gLRaId1d2rC8FwVcejqAWfn/Pe2Kn8Xrm6t7bQQi1sbT8lds3Vx/wZXklqVQyNgzx5TeOVB4P6VvCgdnUbItY0OzUSZDiXY0O6k3WnGZTUQjFUXh+F4uubcQaX8nEvNTJXYCY/VmHdIU1SVR45N0mw38ukbBxZtYR1J+3lx9ie86v8lsmDkswPfQBYN3NX+2YrHW41SkQW11VDe8lGt1bSU5gJxaJCEqjuLttxgejKAioTZWD0mzqbGEJV5AS2oaaz+UxA6X/U1+Y2GeBJMBmSLga7Pvh2TYQL55BnwrskfkzQ4SRhcOKLDCJIIc0Nw6HvazODAwgtrsnkzTQ5r5X+PujB8I6GvrTo6l5iZcAJlCWGr44GVC7SvRSqj8PSpaVY32+iv4jGwVH4z+Q+cixxkn/e97Gt6T0NzhRajyNpWO5kKv9vCeUPQDOKchpJN+LHXtLUzNr+RK6BtBAdiKe0eIjwFB78Fzi7Mmz5Dfms3Gmf6a78kPjRB5z4/rtsqfJy2rNdEX9RX+Qcw2jRhaHZqbqGiDEqF+8junSWvs2pu4omSEaGmkqohgNmdrxyqgoTJcnHNZuqhVpTFU8BTgiB8XVXV6ndPOjqXmMLqYCSR4ZXz2oeKxShilKpvxCsqhJNpnObKwiMnDn95aIxkRuXdV1/6fOp85bAk1sFjMxIaSZPOKJycDDERjPOend2LEoaR9BwvzP6Ig3MPkVKTbHTcwHXN70MWa7tLFraUgjZTaDVKRbur9YrDwtbKavOGMD+/aErOIagZrFVmDgHsGX/R94IApukjQPUbBFkUMJwbw/SDJ0ncdBXyLduxbeqFIy9pLaLuHozKvHALWXtxRs6RSAXg5a+C2QW7Pqa5jlZDEKHvWjLRZlouolW1zqVBX1t1dC4tsWQmvy4F46mq9wC1qJTTu9woqsqJ8RCCoFU4JVHg5aFZQok079tduxOlEVRV5VjoKbotm3AZWrmt9VOIiLhLOnEkUbtvqqWpN3W4kCURGS0qKlAwvlMqDgFcRICsGc3YQU0cVsBulgklUtiMMgzu1x4MjtI0eYAp606EcAzbN35FYiZA99vdOBw+aN9afBKzU5vjc3ZVFocGK6y7SzsOtBl/dw/Mniu56G5t3rAUW0uxOBRlzXG0FKMVyWAEEmQkS0VjnUtNPXdqJkEQvgj0Fx6vquqtK3VROjr1kkhnqprGxJIKMWq3hgZj1ReGXFvpmekwc9EU79jeeVFzZkAzn/nOi8O0Oc2sabFzYS6G0yyXzcI1WY2oaHEWj5+Ywmszsq3bvaj3nE2O8tLsz9jsvJlrve/Fa6pvEer2lO8u2oxykTisOKxdAa/dmK86VmspBW03UVAzGNMhJCWB1VD9/LaMPz8ADlpFUqghDNXwHNMPvob5ly+hNLnI9LQiiwKoCsyc0nYERRmDMD/GELL20Bw4jPzK5yGTgL2/o7WoVEMywsCt4OzANBbAs4T5F50rDn1t1dG5BOQ6cACmgomGxWE6ozAVWllxOB6I8fPXxjifzTy2GSWu6nFzeDRAv9da1Zm8UQrnCvd538vNLR+lydhZ8dg9q7y0OkycmgwxOB0pu/eyGiXWFEQutDhMReJQqiAOHWq29XRuqKowBO3eIWCUEZU0jLwI7dtBSeM8/zDGgTUkDRYEu4XudwxgDz0IXXvLZ/zd/dqfzk6YOFT+Ji3r54VhDs+qcnFYbf7f3qqNqeRwdVc1kRMsHiBEWrZeseLwB8DngS8Dl1+fnc6bmmpOpMvx+pwhTTCWJplWmAjGL1qWUI6z0xHGA3H80RSvjfgBKi4KOQH1/OAM44E49+/orlvIBlJT7Pf9AINg4ra2T9Jj3cLvDnwNp6G57us0SNqcYCk2kwyhRPH3dZ1PxGM1MBtJVTWjAW1+0ZwJgqogZeJYlBBQuQXGkporEYdVqnlKhuSBBxn97mHis0aknf2E7rkJjAaMahxe/GcIT8LqmwFtKN4gCSTTKqHs3KEQnYXdn9LyDAtxdGjmMoKgVQyt3vxiZDFIFZ1add6w6Gurjs4lYLpQHIbiRYKmHiZDiQUjpxaLL5zguUEfB876sBgl7r26C7tJ5pXhOV44O0tGVbl/Z8+Ss/qCqRmenP46R4NPYJM8vLX9j2rOFfY2zQvSq3s9bO1yMeSLcnY6zExY62ja3Oksuu9ocZg4MzU/dyhniltxJVHAnA5BdBbOPVPzem1mLR+ZiUOQikDftWBrIfngX9I78hBnVr+fpvf2Yj/3E3B2a3P+pXj6tT/trZXbRZtWl70EV48mMjPZkRSLG1xVushK5w4rtZRmEa1uYBjVaF/e3MVlop47tbSqqv+04leio7MIwksUh7UcSxMphVRGIZZtLz3vi1x0cTgbTSIK8O/u2chUKM7Z6UiR6UsOj1Xb+Xzx3CxNNiPbe9wLnnsuOc5+3w84HHgUENjheWv+uUaEIUCHy1JxVq60rbWRNtdmu4nZSKpmWymAW51v47AmpoG2isdZUgH8Bd9XyjAkcAFe/y7JUzOkos103RTF3vkiwyEnQdsqOga/D4k52PoeLcw+i0ESSaYzxEwtBG39OFftgrYKu4vdu8FWOc+p1WmufE06b1T0tVVH5xIwU7BhOR1K1DiyMhPLPG84G0lycGSOY2NBxgNxBGDPqibu2NSej2fa0OEkmkgzHU5UdGZvhE63mcdP/4AToWe51vs+9jbdX3Ou0GIU2b2quI1SlkTWtNpZ02onEEsx7Isy0FIssku9EUorh2aDpG20BkYqz/UVvp8g0Oo0wcn92qZq81riF3wMP96K1TPDgOmnNAcOa1nAuz+h5QIXYnLMr72iBPY2La84h62lvGoI2ry/p1/rFgJo3Vz9Ii2eedEpGTRhWQUp25Yqmi6/eUOoTxz+QhCE3wV+AvMb76qqzi71zQVBuAv4WzSHti+rqvoXJc/fDPwMyNV0f6yq6n+p57U6SyeZVi77m9VabqT1EKxVOUxliiqLQzMRrh1oTDQtFV8kicdqRBIFOlwWOlyVxanTYkASBDKqyi3rWxasGr7u/w2/mvh7REHiavfd7PXej9OweOvkTnflgNacYUyORsRhi8PEkC+KY4GWH5caJCfxzfEqzmIxPwaK/66LsgiB9NmDRH/zA5xrJOxv+yADH1yPJCaJvfzP9E/8GhUB1WCFvZ/Nh9PmWNVsZ8gXIRxPM7jmo1zdU2EewdVTVRhCY78bnTcEK7a26ujoVEZR1CJn70giQziRLvr8DcRSmGSxKGKpkNFlnDecCSX4x6fOkEgp9DZZuWdrB5s7nBXHKawmmb5FrhOqqnA0+BReYzc3rN3HHzl/j6fP3IvbWHkztZBrVnnzWcGVcFkMbO12lT3uMBuwGEViSa3MWjpzaDGI1Y1hKmCJa9mFbHgrscEJhv/qh4hGM46dCVyBwyS8mzDt+ZgmzEopjZNwdhaLw0pVw/xzqzRxKJvL1v4iRFETmaFxcPfW9BqQbFlxaF4ZU6GlUs+/so9m//xcwWMqUOM3uTCCIEjAPwBvAS4ALwmC8HNVVY+VHPqMqqpvW+RrdZbAdDhx0StljdJIW+lcNElGUYta92pWDtOZfL+8wyRz3hdFVdW6WwBeHppFEgWu7q0gFOpkNpJYsHIGIAoCHpuRjKJwVSVhAswkhhEQ8Zq66bFuYZfn7VzTdB8OQ3XBUi+dVf6dlFUOzY2JwybbwrMgDjXILCBLAlKkUtAQEPUhl4jBwpiK8OtnGfuHR1ASLqz3fxy5pSWbKWAitPVjTJ56CmfkLGy+lyZv+WJqM0ps7nAyFUowW5I3madj+4I/i86bihVZW3V0dKozG02WtYROBePYC6peLw/N4rYa2NlXHkI+FYwvuWMpRyyZ4Rv7hxAFgT++fR3NpRm3y8Ro7ASPTn6RsfhJdnju4o/ct9IjWhmfkxesnK5ts1dd3+uhxW5mODs3KSkVKoeNMPwCCCKRUBcjf/8vyC4bvf/mvQxHZvHPnMCz+VZMlYQhgKckz7kwhkIQ5ltOK+Ho0MxqmtfWNpeDeXFYKkZLkLPiUDJffjEWUIc4VFW19k+4ePYAZ1RVPQsgCML3gHcC9Qi8pbxWp06mQ5e/OGykcvjz18YIJVL83i3z+XyJtEIinam4KxZLZfJZiFu6XOw/62M2kizLGKxEIpXhwUPjuKyGRYtDVVWZjSTpqWD0Uon7dnRhlMWyquFU/BzP+b7HidBzbHRcz7u6/g1Nxi5ub/t0xfPIotBQXqPXbqz6IV84Y2iSRQwNhKJbjfLCP7uqYktr4tAoiZCKaRbYpU5iUV9ZILtB0sJyp777JHOPHsTkStH5sV3ILcUVVKNB4px3N5Pe3ay31f4gb3WYKs8NOrsueaitzuXFCq6tOjo6VagkhqZCCVZnxeHZ6TCTwQQzYS0Wq3ANUxSVl8/Plb1+MWQUle+8eB5/NMUnrl+17MJQlgRm41M8Mf11jgWfxC438daOP+adA2/P3yNc3evm4aNVNlTRZgi3VagINkKLw5QXh6Uzhw2Jw0waLhxAbd7M+DefwdjqpudfvweDx44wbWACN+3GKhvpJgfYSrq+rE1ayH0qpok/Y417DUHQKoYtGxa+TnurNqPorO1ub7Q6UQUZ2XyFtpUKgmAF/gToVVX104IgrAXWq6r64BLfuwsYKfj+AnBNheP2CYLwOjAG/KmqqkcbeK3OEpiLJElnlLKb6suJ0AJB9oXMRpL4Y0kUVUUsqP4FY2laHOUfUomUQihbOdzWrYnDIV+0LnH4+oUAyYzCTCix6PbcWDJDPKXUNGQppHQOYSI+yHMz3+VUeD9G0cK13vew2/OuBc+zo8/Ni+fqXwBrbSBYjRKioNlfN1I1zLGgI1s8gFnWhGxeeIYmysVhZEZzGi1ARmXoP/0ziZFpmnZYaVl/AXH3zWVvUVhhLD1HJSoe0nnVgq/TeXOxgmurjo5OFXzh8s6OqaxgTKQzHBz2A5BR4PBogL2r5ztrTk+F8Ufrv+eohqqq/OLQGIPTEe7b0b3seYVmg8itG1r56wM/5lToea71vp993vsxihYGWuY3OJvtJvq8Vs77okWvN0gCe1d7K/obNEpu7lBQUgglc4UNicPx1yAZQVh9HT2fa0F22ZBs5ux5REShio8AgLuv8uOODs1dtFZLaY6Oq6o6jxZha9GqlAtkExslkaTBieNKrRwCXwNeAa7Nfn8BzWVtqQtYpVuo0nLFq0CfqqphQRDuAX4KrK3ztdqbCMKngU8D9PZWyBvRqUokmSaSzOCyXJ7iMJVR8nET9RCMp0hlVIKxFO6CyIBQPFWUqwfaIqGo2kyiQRLoabJiMUgM+SLs7Fu4EvjS0GxeFE0G44v6kM1lGjbZFrejeDL0LMPRQ1zvfYBdTe/AIi38IdTiMDHQYufwaCA/J7AQtVpOBEHAks06XMxM3YIbE7HZfNU3vzCExqF14/wxqgpR3/yMoaKAKGIyGfDcfjXGJgO2sa/AqhtBLv9dFy44pa2pGO1au2hovNjCuhBnp7abqKNTzEqtrTo6OlUojLHIEY6niSUzHLrgJ1EQz3BuJsKmTidOsyH//FKZiyR56Mg4R8eC3Li2ua77iXpRVYXT0ae4pr8bt7WbP9rzCXqM1+GQtfXHZpJodRb7A2zvcXNhLkpG0Z5vysZguSyNZz9WwmM1IosCQqp8TtNsqPPeMhnG94OHSCc7ab1nLaaS1k6TLGGUpYrCAKgu/pxd4D9fXTwWUo8wBDCYy/MVK2CUReLmZpqrVTsvMfX8tAOqqr5PEIQPAKiqGhOWx3f1AlBo5dONVh3Mo6pqsODrhwRB+EdBEJrreW3B674IfBFg165d9ffK6RBNZIgk0hU/JGLJTN5F61LRSN9/PJXJf+jPhJNF4rCSKU1OdAayOYiiIGR32CILvteYP8aoP8Z1A16eG/QxFogtShzOZofmm+z1fXhciB7jWd932el+K2sde7mm6X6uabofs1T/rmSf14ogCPR7bRwfDy14vMUoLjgTaTdpWYf1xlg0RHQWo6x9HM1XDkvaZOaGQEkjiyLm0XOYf/QMkduuxdi7C9ttV8HJhzQB2Xd9xbeQRQFJFMgoKnJuN1A2QefV0Lxe2yFUM9XFYduWpf+cOm9EVmpt1dHRqUAkkSaarJwac2QswOB08fquqnBoJMD1a5s5ODJHKrO4W0hVVUmkFZ49M8PTp6YRBLh9Yys3r1++TcMLseM8Mf0lLkRPEhTfwt2rb6PZZuea3jUcG9NupSu5nNpNct4VteEZwDoQRQGv3UggWiwOTQYRqY6PO1VRmPqHrzH7ihnHjj5Qy19jMojVswKtTdWN4JwdmkCUl1mgmetrxU3auzDVK5AvMvXcrSUFQbCQrcwJgjAANO79W85LwFpBEFYBo8D7gQcKDxAEoR2YVFVVFQRhDyACPsC/0Gt1lkYinSGtqESqzPQN+SJs7Khg+7tCXJiLloWsNzJvWGg8Mx1OFOUaVTKlSWTjK0LxVN4ts99r48REKP+Yqqr85OAoiqpy747ufKvqy+dnkUWBWza08srwHOOB8h2zVHYivtYMXs7YpKlGMLqqqgxHD/Oc77ucjx7CKrlIZoe+a4nCXFWzEEHQsowAVjfb6xKHnVXcUwvRRGGi8cphOgETR6B7Z/VjorMYRG3OMicSScc1gRj1wfQJiAdQFYXAr17G+P0nEaQMvVOPYUisAZMTzj+vxU6UziQUYJRF4qnMfFtp2+bi6qS1ymsFUbPM1tEpZ6XWVh0dnQpUqhrmOD0Zrvj48GyUkxMhhmaiFZ/PoaoqT5yc4sxUmGRGIZnW/kuktUis3Hq7rdvFXZvbizaol0IgNcWT01/nWPApms3N/Pfr/ztvWz3v4bi508m5mTCxpMKqKhEYlZxRl5MWh4nIeIkZTR2jNmpGYfzvvk3glRjuXc20/8F7ECq0a5plsXoV0ru28uMARpu2yXuJUGyt1UXtJaaeu7X/CPwa6BEE4dvAdcBvLfWNVVVNC4Lwe8Bv0OIovqqq6lFBEH47+/zngfuB3xEEIQ3EgPerqqoCFV+71GvSmSeW3V2rJsAmgnFWNdtWZKepEq+PBGh3movaDIMNzBsWVgdLF4hK58lVDoPxNN0eTQD1ezXhdN4XZUuXi+cHffnhdLNB4m3bOkmmFV4b8bOly4XVKNPhsjDuL89E+sb+IRIphc/cuLpq6+RsOInDJNecV/z5+F9zLPgkNsnDba2f5Cr33RjFyrEShexZ1cSL52aLBGK705z/+3RZDTTZjNWdN7PU42KWE4WORmcOR1/VbKtbN1YfFs/aYJsNJWY3Jx/Kf5mcnGPsC78iduoC9q4Yhtt7cadOwLP/B7p2QDIM/TfWvBSjJJLOWdwJIjSvKz7A6tUeV5Xyx+ttR9F5s7Eia6uOjk458VSmqgBciFfqMKF56tQ0jx6fosttwWEyYLSJGGWtomWURYySyOpmG71LzCgsZTR2nFOh/Tyw7hP84a5PYzUUr5UGSeSqHg8nxoO4rMvTKtoorQ4zI0rjZjRjf/9jgi+P07xLovkPPlpRGIL2M1qNFdZZUaodPQFaZfESYZTlmhEhl5J63EofEQThVWAv2qzfH6qqOrMcb66q6kPAQyWPfb7g678H/r7e1+osH5GsOIwkKrdgBGMpfJHkRXEzDcVTBGIpJoLxouphI22lueqgxSAVBeDmzlMaURFPZ1BVbT7Rla2QdnosyKLAeV8Et9XAr49MsLHdQZPNyHODPrw2IyaDRDylsKtfmyPocJl5aWi2yAQnlsxwbjqCCjxybJK7t3ZUvGZfJFnWsqmqKucir9Jr3YosGllj302XZQPbXXdgEOubTTQbRFa32JmLJjk5Mb9Y9nqLF5XVLbaa4lAUoN21sBDNtZM21FYa8cHMSa2vZ+p45ephMqJVCYGm0GlM5i6wlv8uY6fHSFyYpvPGNMZVMocG3k5IuJG1574L557WKnulYq8EoyySSGcXJk+f5nJWiChqi0yk5KNRrxrqVGEl11YdHZ15poJxnh/0VW0pXSoHh+d4+NgkV/W4ec/O7rrjrhaDqiocCT5BWk1ytftuNjpu5PZVe7l5TfUK2apmG9ZLOAbU6jBhF4rvJRYUh6qCo3kMy64wTZ/6/QUjJCrOSLr7KvoIXC6YZPGKbisFzR1Uyh5/oyAIqKr645W7LJ1LTSypCa9IslyAZRSVaDKD7yLlIObaMscDJeKwkbbSbHWwv9nGRKB4B0tRIZRI4ywIW58JJYiltNZaR/ZDRxZFepqsnJ4Kc3wihN0sc9/ObswGidlIMh9d0Ww35ts3OlwWUhkVXziZN705NxNGBXo8Fp45M8OaVjtr28rNYmYjCQay9tqqqnI6/ALP+f6Fifhp7mn/A7a77+TmrjsXzCkqJSfotna5GZqJkkgrSCJlsRF9XisHh+fKMqFAE0v7Brx1RVPYTJpjqa2RxWnkBU0YgtYa2r61fC4gms0KT4bpOPMd1LFmuPFzIBlITvlJDE/j2LUW53WbsHmmkM/9jJHuD4Igotra4Po/hmM/09pKFljMjbI4b0ZTzc7a2lxBHOrxFTo10ddWHZ0V5PCFAEfGAvnlZLkZnA7z41dHWd1s494dXSsqDC9Ej/Ho1JcYj5+i37qdq1x30em2cNPAwmaLbc6FN3JXClEU6HUKTM3OP1ZNHKZmQ8QHx3GYXsPpOgvXv09zAF2Aiu2ZC2z6XmpyleXLkXqiLL4KbAOOArnbRBXQF7A3MNF85bBcgIXiKVR13k1zpRnLtmWOlbRnNiIOA7EUFoNEh8vMifEgqYxSJGxC8XlxOBNOMOSLEoxp53cWtEP2e608cXIaUYBP3bA638rwvt29fPGZQcb8ce7a3J5fIDqyQmw8EMuLw9NTYYySyMeuW8XnnxrkB69c4A9uW1s0k5fKKATjaTx2AyeCz/Gc77tMJc7hNrRzT/sfsMV1KwZJYF2bvWFxmJsTNMoi23tcvHhujg6Xpax91SRLdLmt+YyiHB6rgRvWtdQ9Q2g3yVhNcv2L5swZCE/Nf59JalXEUgewbEsp468jqApCZAr19MPMjXQw9b2nkCxGbNtWIYpp5NHHoGmAeNMGiKYwSoI2b3BVfaPKRknU5g0tbnC0Vz7I1qIJ2aLHdJdSncroa6uOzsoyNBPh8Ghgxc4/FYzz7QPn8dqNfPCavnnDsmUmmJrm8amvcjz0NA7Zy9s7/hWbnTdjM8nsG/CuqCBdLrpsMCNqZuFQWRwmRn0M/+UPUKMRrHdfQNp4G/TtW9wbmhya4cxljNkgXrltpcBeVVU3rfiV6FxW5NpJ4ymlLOswlG3nnK2QF7TcKIrKVDCRv6ZANIXLashXL+slGEvhshhotptQ0cxeCnfSgrFUvgr68pA2X5CrNha2K6xusfPEyWlu39hW5PxllEU+sq+fF8762LNqvoe91WlCEgTGA3G2dWuPnZkK5+c137e7h396cpAfvXKBj+zry3/Iz2WFt9dqZP/sD0irSd7W8a/Y7LwJUdA+TLo91oaH2gUBOtzzP/dAi50zU2H6vJVn+la12BjzxzAZRMwGiWa7ke3d7oayLy0GqUhg1ySdhNGXyx+fPAatm+ezg5IR8A9rX4++imJrJU0X4184SHT6KLat/XR88i5EowwnH9bmCjd9Gjmjvb6eimchRjkrDpvXVz+o1BHN5KgdrKvzZkdfW3V0VoiMovL6MkRPVCOWzPCtF84jiyIfvbZ/Rd3bQ2kfZ8IHuN77ANd478v7Cuwb8F4034elYlTjeG0mpkMJRLG80hc9NcrI//4RAhl6bxxHWr0bNrytytnq4DKvGgLYTZdmBrQe6rlj2y8IwiZVVY+t+NXoXDZEC9pJS7MOc6IpkVYIJ9KLyq+rl6lQgnSBa8qoP4bLaiCcSDfUJhKMp3Fa5Hwg63QoUSYOQWsRyc3Z5R5zFLSbrm628dlb1tBZYdbOaTZwx6biqpIsirQ6TYxnW1nnokl8kWQ+WLfDZeHOze388vA4g9MRVrdYOBp8imenfowgfQiv3cz9jv+ATXbnRWGOniYLDpNc0Xm0Gl6bsWinShAEdvU34a6SadTltvDe3T0Vn6sXQRDqb2nxn4dUuYEPqSj4zmh5RZNHYOIwKGmIzcHsWTIdt3H2H04hqAY6bhJwffxeBCUJh3+guZF27gB3L/Kcdm5DtVaOplWaQI3Nae+ZxSiJSLIBvGuqX7vZDZJRq3SCnm2osxCLXlsFQbgL+Fu0ltQvq6r6FxWOuRn4G8AAzKiqetOSrlZH5wri5ESoqmfCUlFUlX95eRh/NMUnb1iFZ5mcR3OoqsLh4OPMJce5qeXDdFk28Nk13yjKKl7f7rikraINk4rR5jQzHUpgLskkDL18mtF/+AUGl5GevcMYV6+Dbe9bcNyjKoJQe62+TGjYpO8iUs+VfQNtEZtAs9kWAFVV1W0remU6l5TCqlxp1mGu3RLAF15EREEDjJfMB44HYmzqdBJqwKkUNKHX4TLTbNdaO31ljqVpkmmF10f8BY9p71FY9RIEoeE5yw6XhdOTWizE4JRmAFMYpbFnVRMPHxvl2clf8kjoEfypcWz0IMghmmxG7Iby6pMsCXS4LIiigMNsIFAhjqMSldxFc7+TlaTuRWxuqPpz469r/yXnTXTSp19ERsWw4RraPtSNvSOOYfB78Pp3NafTREgLt19/D0A+isJYqXJosGiupbnqZDoBgQswdw7j3AUUT2/tPCRB0NxJQ+Pa97oZjU5tFrW2CoIgAf8AvAUt8/clQRB+XigyBUFwA/8I3KWq6rAgCPpOhc4Vy2wkiSwJRb4AtUikMxwdW7l20keOTXJqMsw7r+qsmB24FEaiR3l06ktMxE/TZdlIRv0AkiAXCUOXxcBVPe5lfd8VRclAOoHNKNEkxXD5jkHbjXmzmPjQBKZ2Gz27TiG398KOjy5oQFMT79oromvnSheHXwU+DBxmfi5C5w1OrEQcFlIY/eCLJJf9w7GQ0ozA6VCCZLZiWS8ZRSWcSOO0GDAbJBwmmemSlthQPMWRsUA+wgI0wWg1Sg21UFaiw2Xm1eE5QvEUp6fCOMwyrY55QaYQx7bmfzMu+mgTB7iv6885cbaH2UygqolLt9uClBU6TotctzjsqMNddCXw1GOhnU5AcKz68wWiUE1nmPn5C/h+9jp97+jGYmvBc2uLZmITPgJjr4CrG3Z/Ctzzlc+cqUxFcdg0MC8MQVu4vAPgHUBOJxGm67jZsDXPi8M6huh13tQsdm3dA5xRVfUsgCAI3wPeCRRWIB8Afqyq6jCAqqpTZWfR0blCuDAXxWUx1C0Oj4wGFh1YvxCHLvh56tQ0u/ubuGZVlXD1RRBKzfDY1Jc5HnomO1f4p2x23oQgFK9VogDXDnjz6/8VQUEXTo/vGcyjL6BOHyDd+w4Ma7fRfH0TXuMpxKYu2PPppTmMWpug55pluOiVZyULK0ulnisbVlX15yt+JTqXDfGsS2eOSMlsXyheWDlcubnDWDKDP1osehQVJoPxhmIsQvEUKuDKLizNDlNZ1mE8pXBqojj0PRhL1b0Y1SI34zfmjzE4HWZ9m4OMmmI4cpjV9p2YJCt9xus4craJ23beQ5/Dxv7IEF6bseqgeU/T/K6Yy2JghAqtmCWYDSLei1AlLEPJICRCmplLLfwj5TmBFYidGWP8y78mcWEGZ18Uw7pr558UBM1kxncmO6NYLK5zQt8gVfi91spDko24na4Frw1bs/anZLyk+Uk6VwSLXVu7gJGC7y8ApXdD6wCDIAhPAg7gb1VV/WbpiQRB+DTwaYDe3oUdD3V0LgWjczEUFfrq0GKheGrReYYLkVYUHjw0To/Hwtu3L6/ZiYLCuchrXN/8Qa5purdiXrEgwI4+z4qH1i87U8e1P5UMpukjpOy9TD2RIHLhl6z+2EHk4GEETwfs+e3ymKhGkE0wcOsVky18ORsJ1fMbPCEIwneAX6C1vgDodttvYGIlYrCwchhPZUim52/g5yLJsozAWuQMZephLFBZ8Iz6Y2XXWIvc7KAz2xrbbDdydCxYdlzp3F4oO6e4VDqc2ofdK8N+oqkYsuc1/nHwIaKZAL8z8BVchlbe0ftJDh85xvHxIH1eG75IkjanJuQ2dToZmonkW31lUSiqAFbM90FrVz09GWIuK7DrySRcdnyDWpi9koZN79AcQqtRq6U0y+R3n2T2oReRPQ6639+Pg/2wZk/xQQYrtFfuzJNFAVGoYEhj9S4o5qr9novPkxWH+ryhzsIsdm2t9GFbWiaRgZ3AbYAFrX31BVVVTxW9SFW/CHwRYNeuXStk9q+js3jCiTRz0VTdxiuHLgTqnsFvlKNjQcKJNPfv7F6yM6mqKhwOPMb56CHe1vEnuAyt/N6ar2OoIAoBLEaRfaubL806vhTO75938fadQQlHGDu0iuigj5YbPEhzB8HVCdf8ztJaQQUBVt2kGcHpLJl67nwtaAvXHQWP6Xbbb2BKsw0LWziDJbN+aUUlEEvV7Zr54tAsb9lU3yzWuD9e+fFADKmBD+ZAtsqYE3rNdhPRZIZoIo21Rlk/N6e4VCxGCbdN4VTs59jWPMOJVJg+6zau834Op9ySP2Z1i52jY0Hu2NzOXDTJxg7tQ85r03ITHzk+STKt0OE2F7W6VqpuCgL0Nlnp81p59vQM44H4RcmkzJMIw+Bj81mEAGefhHV3F7du5kgnITha/FgqBjOnoG1LvgIo2S143rKDlvtvQDrw12AeWLgiWYBBEiqb0dQxvC7W08ZjsmviVBeHOguz2LX1AlDoEtUNlPZjX0AzoYkAEUEQnga2A6fQ0bmCGM2aiM3WEZ01F0ly3hdd8LjFcuCsjyabscgzYDEMR4/w2OQXmUgM0mleT0KJYpZsVYVhp9vM3tVXjjMpoI14DD2rdfFkSZ14iZHHW0iE5uj4zD24b9iijWFYPCAv4V5LlLW8YlfXMly4DtQnDr+squpzhQ8IgnDdCl2PzmVAaVWu0Lk0VKGd0xdJ1iUOp0JxpkMJgvHK7Zq+cILJYIK0opBW1DIzmvnrU2hkRCdXOcy3lWZbK2fCCXqriMPCOcXloM0Fo45HkJKreWD1J+mxbi47ZlOHk5+/PsaZqTAZRaUp2zpiMoi4rAZuXt/C48en6G0q3l1zWgwIAkXurW6LIZ9beNO6Fl4+P3dxdxwnjxYLQ9CyC0dfgZ7d5cf7h4tbSpUMvPxVUiODTB5qx3nbtThvuZ7mt1+j/aBTRyEyDatvaeiyZFEsrxoKouaCulzYvHq+oU49LHZtfQlYKwjCKmAUeD/ajGEhPwP+XhAEGTCitZ3+36Vfso7OxeXCnCb2EmmFSCKNrcaG7kpGV0wE4gz5oty9pR1xke2AkfQcD0/+EydCz+GQm3lHx+fY5LypZueV22rg5vVX2HqSCMHIAW1UJIeSZubxc6SiZno/9x5sW/q0xx2LaM8VRK1C6GgHV492jiuklfRKoZ7f5t8BO+p4TOcNQumMYSypkFFUJFGoLA7DSQbq8N4YmtE+5KeCiYri8OhYkAtzC8/ONUowlkIWhXwOUUteHCbprWKmE06kUalclauHWCbEy3O/YCp+lvu6/5xuVzunTv8p1/YN0GPtrPiajVlx+OzpGQC8Nu06c7uFzXYTN65rwWsvFuKSKGAzyUVzmC0FhjeiKBRlL6446YRW8avE5BGtqubpK368pKVUPfpT5l4YZfpIJ2o6g+31X0HzhNZ2MvoKRH1gtEPH9oWvp2m1VpVMJ5BFoSxfCVc3GJZRONtadTManXpY1NqqqmpaEITfA36DFmXxVVVVjwqC8NvZ5z+vqupxQRB+DRxC20n7sqqqR5b9J9DRWUES6QzToXl/gNlIsqo4nArFGavSbdQow7NRTLJY5LJ94JwPWRTY2etZ9HllwcREfJAbsnOF1SqFhRSa1132xAMwfghmzxZt9qoZBWHmFG3bZ2l69/sxbemrcZIqSAbov0GrMpoci4+50KmLquJQEIR9wLVAiyAIf1LwlBNtQdJ5gxJNlgvAcDbOIljBFXM2kih7rJSMonLeFwG0D/HStgxFUZkILM8HeymBeCpbXdM+TDw2I6IA0+Hq152fU2zQajiaDvDS3E95Ze5BEkqUtfa9pJQ43W4LatrN2rbq/fAui4Eej4Uz09owfa5yaC7IJaxW/XNZDEXisNVxCecSpk9oM4bVGHpGE5At2ZDakpbS+IHHGP/2ceKzbmxbemj/8E0Y557XXgdauO3aO7S5wlrD64IA3buhbbMWSXHmUVBVLKWtOcudh+Rdo+9i6lRlOdZWVVUfAh4qeezzJd//FfBXS7taHZ1Lx5g/XjQ/OBdNFpmxFfL6yNKjK6ZDCX59ZJzjEyGMsshH9/WzqtlGPJXh4Iifbd2umqMopShqhsOBxzgafIL39fxXTJKVT6/+ApJQ/zlaLmdxGJqE2CzE/BD3Q3iS0gDquccO4n/iEL1vF5EsZkxbdi7uvfqvL99U1lkxav0LNQL27DGFd7RB4P6VvCidS0u0QnBsNJkVhxXyBf3RVL6yWI3RuVjeWrpwJzDHZChe5JDaCKNzMTrd5qqtGaWuo5Io0GQzljmWFhIoMbGph+HoEb4/8h9JqQk2OK7jOu/7aTWvAmBtm4lPXL+K1c21Yz82dboYmYshCprgEwXy7aG1cJplCif2LtmCoigwdaL2MZkUnH9OyyHs3acFzud2Gf3DJF55nFTMTefv3I3z2s3a32vXvbD6JhANYHYufB2SUcs3zMVYuLqh4yoYO4i1MB7EYNHaUpaTKyBfSeeSoq+tOjp1kGspzVFt7nDUH6t4X1Ev8VSGR45NcuCcD4MkctvGVg6NBPj68+f46L5+prIRWgtFVxSOdwxHD/Po5JeYTAzSZdlANB3AYfA2JAzhMhWH/hEYO6h18FRBVRSmvvcUsw+9hH37Kph6BXq3afOBjdK+DTz9i79enYap+rekqupTwFOCIHxdVdXzF/GadC4x0VS5OIwk0qiqWjFCQlG1Hb1aYepnZ+atpSOJTNnswJh/ce2k44EY//DkGT6wp5etXZWjBoLxNN2e4gpTs708zqKQUFYELxRSGk7PEkxN02lZT7t5gE3Om9jT9C6aTcW28KIgMNCy8BD75g4nvzk6gdtqRBKFugfQC5007WY530J70ZkdLMo0qkl4Co7/HFW2EHrhOJlIHI/wIM51RuwPfALJXdIKa60zU8pohzW3lbuPdl4FUR+W9JA2s9C8TmtLXaLrnI5OI+hrq47OwmQUtSzneC5aWRweXsKs4bmZCD94ZYRANMXuVU3cvrENu0lmT38TX3n2HN/YP4TVKNPltlStWuZod5oJJUN888xfcTL0HE65hXd0fo5NjtpzhdWwmSSsxsuoCyXm10xmItM1D1MSKcb+6UFCL5/G85YdtN3egnDwOei4uvH3dHZBlz7FdrGp519dVBCEvwI2A/leNVVVb12xq9K5pMQqtpVmCCfSVS2i5yLVxWE8lSlrGZ0KJVhVIA5HFzkrMBXUBN6pyVBFcaiqKsFYCldHcbWp2W7izFQYRVUrDpcH42kkQag63xBMzfCC7we8FvgNbkM7n1r1TxhFC/d0/MGifo78dTlMdLjMeLIGP2XzcVUoFIcrOqMQngZ7jXm6ycbGmhKjM0x+81EiR85j7XfivsaHcM1nyoVhvVg8sPYt1SMz+m/AbLRD68b6KpA6OiuHvrbq6FRhIhgnXRJkH0sqxFOZok3TMX+M2Uh5R9NCpDMKjx6f5JnTM3hsRj5z00CR2ZvDbOCTN6zmq8+eYyIY5/aNtU1hVFWl22NBliyET8xyY/OH2dP0rrrmCqvRcilyiauRTsLg49pc4QJMfP0RQq+cpu3Dt9F050549ZtgsGkbso1gtGsdQ/p84UWnHnH4beBfgLcBvw18FKi9baBzxRJPZchUMAKNJtIEawTP+yJJ1lZ5bsgXKROVU8E4q7ItlsF4qqFQ+9L3BRicDlfMW4wlM6QVtaw9tNlu0mI4oqmKgbLBWAqHWUYUBCRRy8WLpxQCqSn2+37AocDDqKrKVtdt7PO+d1nDTD923Sqk7PnqrRwW/nwr1oaiKHD6Ya3a1r6l/PnABW1nsZ5TxZPM/HQ/vl+9hGgy0PbhW/AIDyLYVkHLhsVdn6NDC8CVazjnykboLc0L19G5JOhrq45OFcardBPNRpJ0FsQynZwILer83z4wzMnJELv7PdyztQOTXL7W2k0yn7x+FUfHglzVU9mIRlEzHAo8ykuzP+W2Ld+i1erhM2v/N7Hk0sMWL6uW0qFn6hKGAC33XY9jz3ocVw9AaAImDmvz/2KDHU1dO7Rge52LTj1lCa+qql8BUqqqPqWq6seBvSt8XTrLyGSwelWuMMMQyAetVzouVGHeMMdcjQyioZlI2WNTBfMBi20phfkZBH80VXEeIRCvPDvYnHX8rNZaGoyn8i2lNpOcdy0bjR3ndf/DbHPdwWdWf4l7Ov4Qj3ERVsw1sJvm20JNhvoqhwZJxGbSXrNilcPQGGSScOElmDxW/NzcEAy/UPep4iPT+H55ANe1mxj4q0/StC6OkAzA+rsb3yUUZW0mYe0dtYWhjs7lhb626rypUdXqAmoyWHltLlzn/dFkWetpPQxOhzk5GeLOTW28++ruisIwh9Uks3tVU0VPhfORQ3x96I/41cT/w2awk1IiiKLAurbl6Uq5bMTh+CEtbqoGoVfPMPb5X6IqKoZmpyYMM0l49RuawFt3V2PvaXEvb8SUTkPUUznMKYJxQRDeiha2271yl6Sz3IxkbZkrZRG+dG6WTZ3OvPiJJCpX8CLJNMFY9epeIFbZlCaWzFRs+QjF08SSGSxGaYniMIHDJBNKpBmcjuAtacOYzzgs/qeecwKdi1YWvMFYmlanCV/iAo/N/JAN3nV0CfewwXE9PQObcRiaF33NjdBI6K3TbEBRVRyLjN9YkLmC8aiRA9qfBguMv66ZylTDdwbGXiMRcxOZMtD01huxru1i4K8+hbHdoy0gLz4CTQPgrVZ/roAoa+2hbVuWN4pCR+fioK+tOm9qjo4F2VJhHCSeyuRN4UopnDtcTNVQVVUePjqBy2Lg2jWLW8fTSoqfj/0lJ8PP45RbeGfnn/G+jW+jx+kGYE2rnaOjwSKTPZMskswopWaeVTFWuWe76ARGYezVqk+rqsrsL19k6l+ewtzfjhJLINmy6/Gxn2kh93s+0/gYR+fVejvpJaQecfjfBEFwAf8KLYPJCfzxil6VzrISjKc474uWfdDEkhkmgnFiqQx3bW5HFAViFcxotGMV/FWGwaG6Kc1UqPqu3lQoTqfbkp8brIU/msRVEEeRYzaSZG2bndNTYQanw2V5fjlBW1o5dJg1J9BqA+4hZQST/Vm+dO5lDKKB7e1rIA2iIF00YQj1zxyC9jOWBbwvF6pavnOYE4jVSITh+M/InH2ZmaNOZk9ZkYwKLh5B6rsK40A2wP7885AIwo6P1F4MLB7Nsczk0P4zu/VKoc6VjL626rxpCcVTHB0LsKHdgVyybtW6J8hVDuOpDEO+8q6khTg5GWJkLsa7rupqeL1U1AyiICGLBiTRkJ0rfDcG0US3Z35e0SRL9DfbODOlGfHZTBK3bmjl2FiQwen6rrnZfhmsbaoKw/vL4ilyKKk0E199mMAzR3DsWU/nZ+5BNGXvtcZf11zJV9+ibeI2gq1Zdye9xCwoDlVVfTD7ZQC4ZWUvR2clCMbShBMZtve4ix4f8kW0e/5oitNTYda3O6pWDqG6jXSOSqY01VpDQGstFQWhqslNjplwgv/7yCk+sq+f9e3zzu+pjEIwnqbJZmSgxc7pyVCZwUwgnkKAsmqaJAq4LIaK4vCZqe9h6PsWIUxc03QvH9/6W+zr7+Pnr48tOBvZ4TIvqs2lGo1UDl0WeUF31UUTnoR0Az/X6Cuoh36E/5TA9NFuMrEM7uvX0XJDE1L8nLbgDD+vxUv4TmsVw1p5g/XME+roXEHoa6vOm5nB6QgZRTOeKRRWoEVbVSOSyJBMK5yZClf0R6iFoqo8cmySJpuRnX31h9lrc4WP8OzMd3mg93/SZOzknZ3/Ov+8zSSVeResb3NwZiqM22rglvWtWIwS23vcnJ+NlhntVOKyaCmdG4JE9ers2D8+SOilUzS/+1qa330dQq5zLOqDQ9/TYqI2vLXx9+3U3UkvNQveSQqCsA74J6BNVdUtgiBsA96hqup/W/Gru0JJZxROT4XZ2HHp3RBTGSU/R+gLJ4raLgtnAQ9d8NPntRKrMnMILCjiKonHWvOOU8EESh3ZhlPBBCowPBstEoe592uymXBZjLw24mcyGKfDNT+sHoylsJvkivMCHqsRf7atdDx+GrvkwWFoxiNuJDFzC3f3vJdrWnvpcmiVwnanmTPxcNl5cuzs8zDQYuMXh8aIJRtctarQUFupxYBhpWIZCltKF2LiEBz8Z9KGXiZfUzCv7qD9w7dh7m/LHnCjZlxz7imtaphJwI7fqn4+T7+WWdjoMLuOzmWMvrbqvFlRFJVz2Xir8UC5OFyom2gmnOD0VOMtpUfHgowH4rxnZ3fNXOZCzkde59GpLzGVOEe3ZTOimMFiFIvW+NKoLACX1cCWLicb2p35rGKzQWJTh5NDFxY2drksxOEC7uNNd+3CsXsdrms3zT8YHIUDX9SqjTs+0niuob0NXF2LuFid5aSev7UvAZ8DvgCgquohQRC+A+gLWAWiyTRPn5omnlIuC3EYLOjbPz8bzYvDQDRVNG+Xyqi8PuInUiAOk2kFgyTU7cRZKg6jyTShGpW2QCxFvEobayH+mHbeiRKhmXs/r82Yr5gNTkeKxWE8VTXI3mMzcsp/hO+PfJPByMvs9ryT29s+jUlZTXL6TlrWay2qOaOXdqc53yZSyrZuV164bu508fJQjRm8BjA30FbqsRqR61zwapIIaW2bhfjrE4fJwRMEf/wjvHu7MVz7WVZtDmLs8pb/G7K4YdM7Yc1bIDJVvYWkdSP0XKPPHui8EdHXVp03JaP+WF5clXoO1Jo3zPH6iL+hDVhVVQkn0jx6fJIWh6msi6raa3469hecCD2LS27lXZ3/hg2O61ndYmdTh5NHj0+SSGvXUCpuc2zrLn+fjR1OBqfDRBLV730kEZptl1gcBschMlP2cOiV0yRGfTS/Yy/W9SUj0tMn4ZWvgmyGa/8AbDViryohm7X1XueSU484tKqq+mLJzd3icgfe4PjCCZ4+PZ3/0FIUFXE5btaXQGH8xLAvyo5erZXiXIVe/bMzkfxuWjSZ5n/9+gTv391bt8gtNaWp1VKaI/fhWoucE2ppFdKXrxwasZlkvDYjg1Nhri8YMg/G0nis5eLwQvQYk9avg/UoYzEHNzV/hB2etwEwE9bOm2uRzWUdtjorf1hv6nQWDdUPtNg5Ph6s+eFfL41UDpdl3jAZhWM/1yp17h7tsfA0JGvPSWRiCXw/eYrZXx8E0Yrzfe/BKBkxdS8wn2m0grG//HFBhN690LJ+cT+Hjs7lj7626rwpOTM9v8kaSWhiMJfVW48HQTUjuVIOnPNxcNjPVChOPKXdazywp7ditnGOlJLAIJoQBAGPsZObmj/CnqZ3I4ta22i3x4LLauCWDa08dnwSaCyPUBIFtne7eX7Ql3/MazeSSCv5sZUmm+mS3zsyebToW1VV8f38BaZ/8AzmgQ6a7t6FaCiQECMH4NC/gL0d9nxa2wBuBIsH1txWvjGtc0moRxzOCIIwAKgAgiDcD4yv6FVdgWQUlSdPTheJnXAyjXOlnCPrpHAHLprMMBWK0+owc76COFRV8r3wgViKVEZleDZatzhUVM04JledrNVS2gi5hWA2kiSRymDKCqbZSAKTLGLNxj6sabVzcMRfJFADsRR9Xm1XTxDAbNDaQV4L/IYYI8Qn7+a3rvoQHS53/v1mQgmMsojDLCNLQl6gmQ0STTZDkftqp9vMVSW7kJIosLnTyYvnll49bMSQZlmYOKy5h559QqvqOTtqVg3VjIL/yUNM/+hZMsEorlUJWj7xfgy9vYu/BoNFG2J3tC18rI7OlYu+tuq86Ygk0kyUzOWPB2J5cVhr3rARXh6a5WevjdHpMrOt202rw0S320Kv11bxeEXN8Lr/YZ6e+Rbv7vq39Fq3cnPLR4uOkUWBDpfmxNlkM3Lz+lbOzUQaFnL9zTbG/DGcFgP9zTbs2Q3oiUCcM1NhnJYV8g6ol9gcBEby3yrxJGNf/BWhF0/ivHYTHZ+8c14YZpJw5Mcw8oIWcr/zY9oa3gjuXm1DWrq098s689TzL/CzwBeBDYIgjALngA+u6FVdgZz3RcqqYJHEpReHwZL2jPO+KKrKglWtWMGcYiPMRpZfHPpjSWRRIK2oTAbj+Q/32UgSr82Yb1kcaLFz4NwsF+ai9HltpDIKsVQGp1nmbORV9vu+x339v4uZfm5t+Tjr5Y/yteOjhOMSFLhpz4QTNNu18+Y+tHO0Oc15cShLArv7i91Rc6xutnN0rP7qod0sl5ndyJJQ5uK2oiQjMHNS+1rJwOBjsPZObSi9CkoyxfSPn8PogrZrprHc+RFoX7f4a7A2wcBtYLIv/hw6OlcG+tqqc0USiKVA1ebqFuLV4TmcZgP9XiuyJHJ2OlJmfjnuj7OhXduEXo77hlOTIX762ihrW+18ZF//gvOFQ5HXeXTqi0wnhuixbMYsVq5etbvMRWtyi8O0aFfRSjEa7S4z7a7LIJapoGqoKgrn/+e/ED87Tuv7b6LprXvmx0TCk/DK17W4ijW3w7q7K3sDCEJVx1PsrVrFUOeyoqY4FARBAn5HVdXbBUGwAaKqqo1PAb8JOF1hFm052gqXSrAkuH6kTqesvInNAg6lpeTmACOJ9LL9/HORFAMtdk5OhhgvEIe+cDK/iwewusWGABwf1/6JnpsOI9lPcFz4Ei+MDOIyNJMiiBmwyi5abNq1lraozIQT9DRp1cZScdjuMufPf1WPO99yWoooCmztcvHC2dm6fsZOl5lTJWY3dVUNM2mQlmmXcfyQJgrz507BqV+DUixa40OTzD7yKh0fuwPJYmLVn9+H/Nr/RWjZBO1bF//+9jZtgdEdSXXe4Ohrq86VzKvDc7Q6TLis5RmFhZyaDHEiu16+NuJndYuNkdlo2XFToTjpjEJaUWvmKdfDmD/Gd14cps1p5gN7ehcUhg+O/18OBx7FZWjj3Z3/lvWO66r6LOTuCwqp15PhiiHiA99g/ltBFPHcdjXyvddh70zC6d9ohnLxAMwOatW+PZ+pHVfReTWMHawsEB3ty/8z6CyZqneVgiDIqqqmBUHYCaCqauOBMm8SZiNJfOFyEVUrFuJioChqWTUqnlLqygbK5R36wklUVW3YlGa5qoaJVIZYKkN/s40hXyTfjqKoKv5ois2d84uT1SjT4Tbz9Olpnj49haX3y1h7BsmILdzV8nvc3n0PHS4HR0aDgObuWZp1mMoo+KMpru4tnjfM0WI3IQrgtZtY11a7N35Vs42XhmbrstvucFs4NVksDuuaN5w6Ch3bFz5uIRJhmDlV/niBMEzNBJn+4TMEnjuKZLOQuGsX5p4WDP6XtNaS9XfV914Gq7aTWDjH6OrWWkmXS+jq6Fym6GurzpXMqD/GuD9OKq0Urb+lzEaSHByeH61IppW8UCwlo8BkKEGmjo3rWsxFknxz/xAWg8RH9vVXXUPjmQgm0YIgiHSZN+A1drHb8678XGElREEbI3lDkIxCMgwmJxjMmmgLjMDkMQiNo6oqcw+/iuy24bxmA+4bNsGJX8KBx7TXG+1aznD7Vtjw9trzhWYntG3VHM+jvvLnba0r8RPqLJFad2IvAjuAg4Ig/Bz4AZBfxFRV/fEKX9sVw+nJyh94l1ochhLpivET1ar7heTaSpMZhVA8XdXxs5ScKU09ZjT1MJdti/VYDbS7zHnH0kA0RUZV8RZkC6mqwo71E2wOrqPDZWFavBWH6a1sdd2GJMjYjJaiSqAoCLitxiJx6IskUZkfMC+tHMqSSJvTzI46MpIEQcBiLG8XLUUUoM1hKuu8WFAcphNa+0fbVlhqhMX466BWVrFKMsX0D59l7pFXAfC+9Rq8b78GyWbWnE2HntFyiRwdC7+PrUXLKzRaNaMb/3lNWPbsXfrPoKNzZaCvrTpXJIqi5gWfL5IknspUXKeSaYVnz8w0lENY6lraKP5oki8/e5ZkRuHTNwzkZxgLUdQMr/l/w9Mz3+LW1k+wzXU7V3vuruv8rU4TJvkyjlNSMnD2SW3j1WjX/vMOgFxilhMPwKmHNXEIIBm1uImUVtFVkmkmvqYF2zv3bcS5cxUc/JYWa9F3LWx6d2OzgV27tLXd3lpFHDboaKpzUahnm74J8AG3og3OC9k/9QUM7UPwvK+8TQIgfInFYem8YSNECyItZiKJusVhzpRmqsGhcn80yd8+dpqP7uunv3l+YNyfrUR6rEbanWZeG/Frrlk5p1K7EUXNcDz0DM/P/AszyWHe2/2fGbDvYgPvKnoPs0EsE3tuqyGfdQiaGQ1AsyNXOSxfDPau9mIx1rdI2IzSguLQapKRJc1Yp7AVd8EYi9C4JhAj04s3b4kHtR1D35myp3IVY0GWiBw9j3PfRlruWIuhb818tMSZR7X203V3Lvxe3gHou25+JsHeov2no/PmRF9bda4oTk2F8m2fqqoJutUt5fPhLw3NLrjulTLmj9WdPVhKMJ7iK8+eI5rM8InrV1Wc2zsXOchjU19iOnGeHssW2kyrGnqPnipxFZcNIwfAP1z82MRhzfXb06d9H/HBmUcgVSDEM0ntP7TuoAt/+1Pi5yZovvc6mu/aDM//rRZrsfk+6L++sVgpR/v8e9tbYep48fPmbOVS57KjljhsFQThT4AjzC9cOZZW+38DcW4mQrpKkHskeYnFYXzx4jCWSufvVHzhJKsXSCQo5PxstOF5w8HpMIm0wuB0uEgc5qp67mzlMHFOwR9LZdtXM0yrz/HouR8xmxyl2djHOzv/jFW2qyu+h0mWytpEPVYjpwoqvzNZA57mbEWyVEwCdQtD0FpdoXYV1ZF9D6fZUPR7My1UOQxmjQ2Do42JQ0XRgupnz2q7iCWo6Qz+Jw8x9+hB+v7DA0g2M/3/6UOIo/vhyD/ASDesuwuc3XD+OejZrX3wV0MQtZmDjm31X6OOzhsXfW3VueKIpzL5kYwcoxXE4UQgXnXDvBaL9SgIxVN85ZlzhOJpPnZdf8XMwYcnP88rc7/AbWjj3q5/xzr7tWWjMnazTCSRrtpZVS3L8LLAN6hlDJaSisLg49C0CjyrYOjZvBAsJR2IcO4/fAM1laH7j9+NY8caOPBPWtbhnk/XnimsRvee+a/tFe5R9JbSy5Za4lAC7BQvXDn0BSzL6anqHgKxpFIUq3CxWcpgdzSZwWs3MRdNLuhYOhtJklYUWh3aDtBglaD4WuQWk/ESi+u5aApZ1FxDO5za+ScCcWYjCSQBXgl+F7Nk592d/471jn0IQvVqm9mgVedEgXy7rcdqIBRPk8ooGCSRmXACp1nOC7NqhjP1Uklclh1j1o5xmGXGC7Sa2bBA5TA4qv0ZasD9Pp3QWk+CY2VPqYpCcP9xpn/0HKkpP5Z13WTCMSSbGVEW4dzTYG3Wdh1f+rIWWKsqmqNpNcwuzaLa1sDugo7OGxt9bdW54jgzFSZZ4sg+HoiX3eMcuuC/aNeUTCt84/kh/LEkv3XtKvoKYirimTCSIGMQzay27cQhe9nteWfVucKre9w4LQaOjgXyru45vHZjQ5vCF5V4AIb31z5m9pz2Xw1kl42mu3fj2LUWU6dXE5Izp2DrexYnDL0DYPPOf2+0aa2uyYL7w1qbyjqXlFp3ruOqqv6Xi3YlVyAz4cSCAixyCbMOl1Q5TGawmSQEjPlQ+FIS6QxPnJjmuTMzuK0G/tUdWmB5ahFD5TlxOFFiZOOPJnFbtVgJr0PC4H6BRwNfwhP5Azw2Cx/q+0uccktdhjkmWUIQBGwmmVC25cVj1RaKQDRFs8PETHg+isMki0sOlrdWaEsFtJ6c7DXnBKSj5N+JudZ8QyKszfuB1laaTi7s8hkPaG2g8WDZU5logvP/5dskLsxg6m2h50/vw7Z99fzvdea09j5XfUirAo6+rO1Itm0Bq7fsfIC2oHTt0k1mdHSK0ddWnSuOSjOB6YzKVChOh8uSP6ba/cJSePzEJPGUwh2b25Czs+mqqvKjVy8wHojz4X19rMp2HGlzhb/m6Zl/Zqf7bdzQ8kHW2Hezxr675nt4bEbsJplrB5rZ2pViKpQgnVFJZRSabJepg3ZuzjCzuHu9TCzBxNcfwXvPbsx9bTS/Y6/2RGQGjv8cWtZD77WNn9hog+4Kv297K8wWiEN93vCypdZd2xvMn3f5qaenfilZh+mMQiKtLLp6tZSZw1gqg9tiwGKQ8EXKK4eHRwP88tAYwXgal8WAP5ZqyNW0kEgizXQ4gdUolQXdz0VTuK0qL8/9ghd8P8Dc4SOVXoUvPkOTrQ2Xof6dp1wlzl4gDt1ZcTgbTdLsMDEdSrC1S3NgW2rVEMBmLD6HKTGLIzqMPTrChdZbSBvsBeKw+NiahjSF1UJVhdAYePqrHx+d1WIp0omCl6kkR32YupuRrCasG3tpftc+HHs2IJRWu4ee0Xb9Oq7SZgZ7rtH+K8TVA85OsHi0//RZAh2dSuhrq84VRTKt5J3ISxmdi+XF4eHR8jGFpfLq+TkePT6lvZc/xgev6cVqlHny1DSHRwPctbk9n5FYOFfYa9nKOsfeut7DIBVnGjvMhrLN2suSqePa2r4IEqM+LvztT0iOz2Hb2Iu5L9v2qSrw+ne1cZBt729sxhC0+4PVt4DBUv6cvU0bZwHN1MaysLGfzqWh1t2vnkq5APH0wj3yi3UsVVWV5wZ9NFmNbO2unSVUiVgys6gKXo5oMkOHy4LVKHFmKoyiqojZD4mpUJzvvjhMp9vMA9f0MTwb5aHD48RTyqJaL4azuUc7+zw8c3qGiWA83x4yl5gi0v53PDIZpMeyGZP/g4Tm+vHH06zqNdU6bRk5wWk3y5BdwzzZEN+5aJJIIk0slcmH2tbTEroQhYY27TPPY4uOzj8XHydgWFtDHNaoWpa2hQZriMNMWttdzApDVVWJHh1m+kfPEDs7wcBffRJjq5v2j96uRVdEp4rnA6KzmivqmttqVwF79+rh9To6C6OvrTpXFFOheEXnc9AE267sn5UivZbCmD/GT18bZVWzjZ19Hn5ycJTPPzXI3tVeHjk2yVU9bm5Yq40sPD39zzzn+y5uQzv3dv171tn31b1ZndskvqLIpDTDmUUQfOEEY1/6FaLJQO/HN2NTfgb7n9Ycx5W0ll+4/QOLE28911Q3mitsI7W1NC48dS4aVe/0VFVd3HZEAwiCcBfwt2gzGF9WVfUvSp7/IPBn2W/DaKHBr2efGwJCQAZIq6q6a6Wvt5R4amGf5vAih6xfGppjdC5GLJlelDhcSkspaOLSapTw2o3ZYNpU/gP0THam8IN7+vDYjMxldxRDidSixOF5XwRJENjV18Qzp2cY8c+BeZAO0xaiMTtdwm5u7bmdPts2HolM8sSItovobbDVIxcqX1jNy2Ud+qOpeTOarFOp3bx0cWjNvpcp4SsShgC22DgBx9r8+9hNctE8ZE3b7NI5wwozhHlGXoB4QBOFx4eZ+fFzRE9cQG5y0P6R2zE0ObQ2kuH9muNZMgzr74E1b9E+vM8/p52n77rq72Hx6MJQR6cOLsbaqqOznNTKLY4kMsxFkhxe5lnDWDLDtw+cx2qU+MCeXuwmmSarkX8+cJ4HD43T5bZw9zYXsUwQq+xivWMfRtHCLs87kMXGqn5u6xVQJSxl8iikG8+TDr1ymtG//zmWtV10vbMFw4WHwNWric2RFyGT0MZFCs1k6qV5ndaKWg2LR4vOyCT1ltLLnEs2DCQIggT8A/AW4ALwkiAIP1dV9VjBYeeAm1RVnRME4W7gi0BhL9stqqrOXLSLLiGWXJnK4ZHRQF6AzUZSRBLpshbHRDrD6FxlG2lYWktpOqOQzGhVQK9NE0q+SDIvDgenwjTZjHhyjp5ZcROOp2mtnQtfkfO+KJ1uM3ZLCmvrEzyffBbxQob3tX0JENhh+zh9NjdAkUV1I3MAgjAvDqtlHebFYT7jcOkD6JIoYDGKeKaOlj1nTsxgkZT8XKMgCNjNcn6O1VQtyiI6W2xFDdr8YTyoWUMX4hvU5gWBtD/M8P/6AbLTStt923BvMiDGT8ALj2sW2IIIrZtAkODkQ9rCs+5uTVy2bam9i+jqqe8XoqOjo6NzRVFqFFfKS0OzzEaWtiFdiKKqfP/lEYKxNJ+6cXV+ze5vtvE7Nw3w7OAU3o5X+cr5/8CAbTdv7/wT2swDtJkHFvV+nitNHOYyjhtAVVQEUcB+1QBtH7kNT68P4cxD0L4ddnxYyzpUVYj7wehovKpna9a6h2ohCJooDI7qZjSXOZfSKWIPcEZV1bMAgiB8D3gnkBeHqqo+X3D8C0D3Rb3CBainrbTRrMNjY0EOXSju2x/1x1jXVqy6hmainJkKVxeHS4qx0H4ui0HKt1j6wkkGWiCjqJydibCt250/PvfBHVrgZw3GU7x6fo5d/U3516QzCqNBH/2rX+XzZx9H8kYQ4pt5YP0nCAW0Ywo/uHOOpdCYODTJYr7FpLQi6LEamIskmQknkQQhb1KzHDOHAG7FjyU+Wfa4oGZoUmaA/vxjDrOBYCyNURYRq7ncVnMnDY4ViUM1FiDyi38mcvgsbR+4GYPHQe+/fg8WwxDiqZ/AabQ5QnubFk3Rsxcsbm3m4MiPNMOZmdOQjGj5RrVwXVb/a+ro6OjoLAPRZHpB473FmtCkMwpyBdO310f8nJwM8fbtnfQ2FUdIBDjClOPLHJsdps+6jT1N71rUexdyxbWVThypGklRifDrZ5n6/tP0/tl7kR0Wmvom4fTD0LUTtj8wnz0sCItrJZWMsPrm+fPUwt6qiUO9cnhZcynFYRcwUvD9BYqrgqV8AvhVwfcq8LAgCCrwBVVVv1jpRYIgfBr4NEBvb++SLriURGphcRitM+swlVE4cHY2P39XyOhcuTgcnA4TiKWYDMZpc5Ybfyw1xgLAapRwWgzIopCPsxjzx0ikFQZa5i2jHQWVw0pkFJUXzvp49PgkibSCL5Lkvh2amBj1x1AkP5PSg6yz7iUzdxvHhp20b1/H8LjWfVX4wd1kN2KQBFIZtUFxOP+hVRps77EaOTkRYjqUoMlmzNtyL5c4bA0dp1q4hzc1VfR97ndZe96wmjgchdYNqDE/4Qd/yMw3vkf8zCiy10Hz269BsluwrW+HJ74MTQOw82OVW0EFEbbcD7JJE4i2Vq1dpBqySd8F1NHR0XkDMrFA1XAxpDMKjxyb5LnBGR7Y08emzvlNTUVVeeLkFO1OM9esaip63YuzP+WxqS/hNnRwX9efs9a+d1EmeIUIArgtV1DlMBWDqWMLHweoGYXpHz+H72f7MfW0oMSTMPG0Jgx79sK292rr/VLpvx5MdbaN2du0iCu5Mc8InYvLpRSHdWc8CYJwC5o4LCxfXKeq6pggCK3AI4IgnFBV9emyE2qi8YsAu3btWtYMqXpmDuvJOvRHkzxzeibvoFnKZDBOMq1gzLYZ+sIJ/FGtMnhqMlQmDqPJdL5FcjHk2mUtRglREGiyGZnJzhWemdZkTmHF0mKQkAShYpV0Ihjn+y+NMBGMs67NjtUoc/DCMKbWh5DkBLbg+1ESHXyk+wt02bt4OT3La5lRZiNJ/NEkkigUVfpEQaDNaSYYSzUUM1EotkyylBeYoInPUCLNRDBOW3beUBDAblyG/z1CE9iTU1XFoTNVXFF0mmUEJY1ZqrJYKRkIT2hfJ0Jw9gmt6icZITRO4uEvM/rX3yQxPI2hxUX7J+7EfcMWhJw4HnpWe101YZhDEGDD28HZpUVV1FqAnV36YLmOjo7OG5DlFoeTwTjff3mE8UAci0HiF4fGWNNqz9/fHLrgZyac5IE9vYiCQCwTIp4J4zF2sNFxA6qaYeci5gqrYTfJFauXlx3pJMRmNYdSZeHN/9RcmLF/+AXREyO4b95G20duQxx+UnMt796zfMKwbTN4+uo/3tZSbHinc1lyKcXhBaBwUKkbKHPVEARhG/Bl4G5VVX25x1VVHcv+OSUIwk/Q2lTLxOFKEq+jcghaa6mrys5UMq3toNVyFlVU7QO616u1VwxOR/LPXZiLlc0kvnDWtySn0lxbqdWgnbPZbsqLzcGpMB0uc9HcXm5WrpK4fez4JP5Ykg9d00t3S4rnpn+I1fprDofSbHHdytBsGK/NSJe9CyBviT0eiGsxFhZD3iU1x7UDzQ3Pcpaau9hNMnNZgZ1rW52NJNmc3cG0GKTqbZ2NMHaw+uwgYCGhzRBatR1Sh9lAs/8QrYoZ6Ch/wciB+UyjM4/AuadRzK2kresxtnmQzWlEo4GOz9yDa9/GeVEIkIrD4GPQshGaVi987YKgtZ0shN5SqqOjo/OGZDK0POIwnsrwwlkfj5+YwiSLfGRvHxajxBeePssTJ6e4c3M7iqry+Ilp2p1m1ndYeXnuFzwz/W1azf18sPcvcBi8XOO9b1muJ8dlbUYTm4OpE1pXUC7XuE6mvvcksXMTdHzmHtw3bNE2kk/8Ejp3wvb3NyYMRUlzQ29ep91/BEYgcAEMVi3LuBEkWctA1rmsuZTi8CVgrSAIq4BR4P3AA4UHCILQC/wY+LCqqqcKHrcBoqqqoezXdwAXNVQ4kc5UtXYuJVJDHJ6YCNYl5C7MRen1WklnFIZ88+JQVTX30O097vz5JgKLrxrCfFtpznnUazdyajJEMq0wPBtl7+ry0HO7SSacKJ9z9EdT9DZZEe1H+PzZv0JRM7QI+xga3MfV113D131D+YwigFanCQGYCMTwR5P5+b9Crsr+rI1Q2qZpKxKH8++RM6NZlpbSeABCEzXFockgah+0WXHoTE3hDA/iFE2amYy3YMB++hRMn9S+TkbJDB7Af8rG7C+eQfIcY9X/+BiS1UT/f/pQ5Tc79xSkopoT6XIhCLo41NHR0XkD4o8miSUX7pCqRSie4vlBHy+c9ZFIK2xod/Duq7vyOYI7ej08e3qGq3vdjPnjzIQT3LZjjq8N/W98yRH6rNu5vfVTy/HjVKTSPcYlJ3BBmyus5i9QBTWjkIkmkB0W2j54K81v3Y1JHocXv6i1onZsh6seKBeGzi5NgJYiytB5lSYKC9tA3dm6TiYN4iKqj9amhY/RuaRcMnGoqmpaEITfA36DFmXxVVVVjwqC8NvZ5z8P/H+AF/jHbF95LrKiDfhJ9jEZ+I6qqr++mNdfT0tpjmpVrngqw4mJ+naDxgJxFEXl/GyUdImYPDMVZkuXi3A8zesj/rqvqxqx7JykNSsOm20m0orKoQt+0opaNG+Yw2GWyxxS/clJ/Olhutzr6bJsYKvrNvY23Y9FaOWvz57kh69cIJrM0OedHzg3SCLNDhMT2crhhvblCVIvqxwWtKp6bOXicDkyDnPxEsYakRRmWdIWgo7tkElhGT2AJApam8v557UPUYsHwtOaayia6+js937E3AEPSkrE2hbH+959taO1kxFt57Bt6/wH+3Jga9VnB3R0dHQuU3zhBJIoLMp0ZaJGhEU9TAbj/OOTZ0hnVDZ3ubhpbQtdnuJw9Lu2tHNsPMDPXx8jFEvjbTvMi7Fv4zF0LttcYS0uWeUw4gNbyUa7qsKFl2HySMOnS03NMfp3P4RMmr4PdSMnAshTx7QNYbML1t4Ja+8oN42xuGHdHTB7ToutynUmmZ1amH0tIVcr91jniuaS/s2qqvoQ8FDJY58v+PqTwCcrvO4ssH3FL7AG9baUAkSqRF4cHw+WCb1qJNMK0+EEZwtaSnMk0lo18fRkiMzSNvkAiKYyiAXRD96sY+mBc7OIgmYnXYrdJDPq1+IVZpNj7Pd9nyOBx1GaVuG2/nscBi93t/9+/vib1rXwqyPa7FxfiRtZh8vM2ekI4UQ6L9zMBrEhQV5KaeWwUPw5zDKSIJBR1bw7a6lpzaLI7sQZ5coLmyQKGCURItNay+foK5AMY5JFDJKgzRUMPgFrboezT6Bm0giCQPj1s/iencCxxoj33tuwDH8NvLPV5/5UVWtBTSdg/d1L/7kK0auGOjoXnYUygguO243m9P0+VVV/eBEvUecyYC6S5ImT06xusbGjdxHicInzho+fmEIUBP7o9rW0OCpvItpNMrdstPGbkydQEl28d+PtZGxOrnLfiSSsvHC7JE6liTCc/CWY3dC9G5wd2j3AuadqZxZXIfTiUca/8EvUjEL7rgDCudNgcmoVv55rtOzBam2k7du0P5tWaRvRg49rf/ZfD9W8D3Te8Oiyf5E0JA4rVA7jqQynJ6vZlFTm2HiQ6VDlltGXh2aXRRiCZkhjNkj53Tpvtpo26o/R12StGM5uN8tE1XF+PvZLjgWfQhJkNtru4sDpbbi2l3/A7F3t5bkzM6Qyaj50PkeH05yP88i5iDXZjIz5Ky9Ubqshb9BTjdJrtpVkHbqsBqLJdF40mg1LFIeKAiFN/BpEEUkUyJT0IecG8LXdwpfAdyb/3jmzHTXmJ/rD/8fsg/uxburFe88eXD1hrG+dwnj7p7UP/9k2GH21ckh9bA4O/0BrKeneDc5O7XFPv2Y0I0paruHEIa262Ci6ONTRuajUmRGcO+5/oXXn6LzJCMRSPH5iShsH8UXZ0VseUeCPJhmZjbG121X2XDiRZiq4+BGVyWCcI6MBblrfUlUYZtQ0r849xMuZb+PotWCd/rds62pBFN626PdtBIMkLE+XUKOMvqKZy0V9mkGMq0fLF2xwrlBJppn69iPMPXYYsydF18f2Ydy8G4y2+mYKTY5i/wGLGza9s75ICp03NLo4XCSNVLEquXgeHQuSrndoMct4FXEELJswBG3mMNdSCpqDZs7dc6C1ssOlwyQj2o5zMvQ8uz3v5BrvvUzMGnkhfa7izpxBEnlgTy+RZKbMcKbdNd92kpsHaLabqorDdpd5QXFYq3II0OYwEU8b8oLYXKMVtC4iU/PtGWhCMFZSQS4SoFlhmHtcUhX8zxxh9tcvkzg/heS0Ytu+GlQFYfhJjF3tmjAUBOi8Gk79BmJ+7cMdtKzCoWe1AXRU2PQuWHWj9pwgaLuJxoKKrb1VC77P1P494unXdiQFQZtH0GcHdHQuNgtmBGf5feBHwO6Le3k6l5JkWiGcSPPUqSkSae3GIJrMMBWM01ribH56KszgVJhuj6VovEJRVJ4/M9PwPUohT56cwiCJXD/QXPH5wfDLPDb1JXzJC/Rbr+Lmnk/g3dBfdj+wklySqmFkBmbPFj8WGKl87AKoER+Rlw7RtD5Gy8fvReza1tgJ2reVdxzpwlAHXRwumqVUDmPJDGemGtshupjEUhksBcJFEAS8NhMTwTgDBREWk/FBnvP9C2vte7Gbd5Ka28t7tt5Pf5NmU+yPZnMKq5jx9HrL21NBayvNkVuw3FYDokBFE6AOl5kT47V/n5XcSgu5b2c3asG5TbVyBuuhZLjbVEEcmqq0m5oNIv6v/Jroiycxdnlp//gduK7fjGg0aLMI4Sm4+sPzH+qdO7Tdx/HXtCBaVYUjP9RmFls2wNb3aFXCHLbWYmEImshbdZPmZqpWuSGwerXz67EVOjqXkgUzggVB6ALeDdyKLg7f8IzMRnnx3CzJjFL14/v8bLRIHCbTCudmIigq7D/r467N7XmH7sOjgUUH2wNMhxIcuhDghrXNWCtU5s5FDvL9C/8Rj6GT+7v+P9bY9yz7XKFRFlnXZkcUBAanw0QS5fdsnksxb3jhpSW9XFVVgvuP49jSjnTwi6y6O4J47aeKzevqwWgD75olXYvOGxddHC6SRsRhPKWQzijIksjZQx31IAAAcc1JREFU6TCvX/Ava6VvIU5NhmiyGsvaN6sRS2bKxFOz3YgvkqCnycJ47DTP+b7L6fABTKKVPus2vCYZVCOp9Lzo8MdSCICzwYBZh1nGapRIpJSCUHgJq0kmXCEuo8lmxCSL+V3SSpSKPUkUsBjFvBObtSTTcMmVw5K5gUqOpYWCNT40yezDr9Jy73WYHVbMd+/Ce/M2bFv75xfN2Bwc/4U2D9Bx1fyJ7K1ae+foq5p4O/GgJgwHbtWyCksXXU9/5Wt292jBuMP7Kz/fu1cXhjo6l556MoL/BvgzVVUztW66BUH4NPBpgN7e3uW6Pp2LyMhslOfOzCzonj7si7Kz15MXgEO+SN7zwB9NcWQswLZuN5PBOMfGgzXP9dyZGabDCe7Z0jE/HlHAkyenkCWB69e25B+LZoJMxc/Rb9tOv3U7b+v4EzY5b1z2uUKbSWJdm4M1rfb8eMbmTiej/hinJkNFbu4rXjmcPqWNcuQyhefO58dNFkMmHGP8K78h9NIp2q4TaOoPIV7/2cZyBnO0bVmc06jOmwJdHC6SWAPiELRduzNTYXxL2I1bDHORJF9/fgiAfq+VXX1NbOlyVfxAzxFNpstmBG7b2MbOviaemP4yL8/9DLNo44bmD7LL8w7Mkj2fg1go3vzRlGb20mBeoCAIdLoszEaT+RYTkyziqCAODZKASZawm2USVX63glBZnNlNBmLJyjMVS6ocpuLaLEEBlX7fRgECzx9j7tGDxE6NIpgMOHauwXz1AIa1XcUHz52Dl78KmSTs/Hh560fnDjj+czj8Qzj/LPRdW1kYQu2FpHUDpGMw9lrx4941mgjV0dG51NSTEbwL+F5WGDYD9wiCkFZV9aeFB6mq+kXgiwC7du1afA+hziWhXmEImnHdeDBOl1sb2zg1Wdxtc2wsSLPdxIFzvqrVR9C8Bx46PI4KXJiN8qG9fUUiyxdO8PoFP9cONGM3yfm5wmdnvo0giHx24OsYRBNbXbct5kfOYzGKuK1G3BYDLosBh9mAwyxX9AsQBIFuj5Vuj5WR2SivDs8RSWRW1ql06DmYOaWtwc5uaFkHoy8v+nSRI0OMfeEh0sEordeb8XSdgx2fXJwwNNq0sRQdnSro4nCRNOqceeDs7ApdSW3GA5qD6J7+Jganw/zw1Qs8cXKK37t1TUVjGShvKx2JHqHVtpo2pwMhtBWb7Gan+22YpPkqoSNbaQwVisNYsqGduSabgdmINvN299b2IgFukqWK2YO5yqLDLFcV3kZJrNiyYjNJTFfoRhWFBQxpVFUbHI/7tTm/ptXzO4MAobGy1kyjVHK+VJqZf/dVMnNhDG1uWj94C+4btyLZKkR3jByAw98Hswf2fhYc7eXHdFylicPzz2oht1vurywMbS3awlCLzqvB6NBsrVUFJKNmZqOjo3M5sGBGsKqqq3JfC4LwdeDBUmGoc2Uz6o/VLQxznPdF6HJbmAzGCcaKN1oVFZ48OV3z9RlF5acHR7GZZO7Z2sHPXhvlH58c5IPX9CIKAqcmQxy6EEAUBK5b4+VM+EUen/pKdq7wam5v+xQGcenRR3tWNbGmiv/BQvQ0WelwmTkyFqw68rJkzu/XhCFo9wKBkUXPFQLMPvIqk994FGNnE/1vt2NJvqqNi7RtbvxkkkFzQNdjKHRqoP/rWCSJdGOVw0vFeDCOANyztQODJHBsPMi3Dwzz6LFJ3rqts+z4jKISTymYjSJDkdd5bua7DMcOc0vLx9nrvY91jn2sY1/Z64zZ+IVC851ANEWn21J2bDU6XJa8OOwoMKURBe38leIl7Cbtw91prv4hX03oVXMpW7BqeOax4g/6uXOw/q3zH7YVrKgNSoym154hMQWRO25AMMo03bkTc08Ltq2rECpVV+MBOPZTGDsI3rWw87eqCztrkyYKBWB7hZDbHNVaSktpXqM5mQ0+roXgGpYnb1JHR2dp1JkRrPMGJp7K8MKgryFhCHBhLkY6o5RVDevlhbM+Rv0x3r+7h23dbjpcZr71wnm+8LRmsCIA3R4L9+/sJiGM8oML/5kmYxfv6f6PDNh2L9tcYVcD9xWVkCWRq3rcy3ItZQwfgOkTy3IqVVUR1Ay2ThXPLhetG8cQk7Ow5i2V3ckXQhAXzi7U0UEXh4umkZnDS8lEIE6TzZhva9zc6WLPqiaeH/RxVY+nLJA2nsog2U5xSvwKL4+cxi57ub3101zlvrPqe4gCWM1aW0dOHCqqSiCWYlOns+5r7XCbOTpWPuuQE2sOU7kAzAlGh7n6P+VSp9IcpXOG+ferNW8YnS3fAYzOwtAzMHCL9n2BOEwHIvh//RT+Jw+RDokYTRn6uo+R7tmG5/qtYG/TfoGFKBnNafTUQ9rX6+7SFoOFXMR2fLj289BYC4qjDTa+feFKo46OzkVloYzgksd/62Jck87F48C52Zoz9tVIZ1ROT4W5MBdr+LX+aJJHjk2yvs3B1i4t+qLNaeZ3bxrghXOzeO1GupsUptJH2ehcA7i5v/s/stp29bLOFTbZDFiMS/QEWCkCo1ps1BJRFZXZXz5H/NgxOq8axpQK077OAJ510HHX4jt5+q4DV9fCx+m86dHF4SJIppWLaiizFCYCcdpdxVWfOze1c3w8yE8OXuB3bl5TNBMYS2YwNj1DklnuaPsdtrvuQBZrt4Y6LQYkUcsLCsW1yl8kkSatqHW3bdhMUtXqX06sVaoczreVVn6tOTGDSa5stFAY12GJT5E0OMhIlqpiEoDJo5UfnxvS5vQ8ffm8wNCrp7nwNz8FRcXSDumbN2DokxCiJ/AMPwLDj2TfvElzAs0ktNcmwtrXLRtgy31aK+hyYPVq1cBGMC2udUdHR0dHZ/k5Ox1mdBHiLsfrI/6aM4WVUFWVn78+horKO7Z3FlUArSaZm9Z7eGXul3zzwndIqUl6rdr4yVr7nkVfZzUa6Ua66MycXPIpUucHGfvCL4kOx7F3xVAdvQirr9VmFqUlGOh0Xq11BOno1IEuDhdB/AppKU2mFWYjybL2CYtR4u3bOvnOi8M8d2aKto5BXvD9iHd1/RnRlJ34+Hu4r2sDmzzeyicuwWUxkEwr2E1y3pgmENNEYr0zhx6rEbNBqhhXkTOTqTRzmGsrrdQiKigpOqafw9oko3kyFGNVI3j9h3BEhpEyMZIGF6Ntt2CWrWXHAppwK80nKiB16Cn8L57H1GzEuXsd1uhTNK0N4d63CtNNH+C1yTiBlIKr7S0gRMA/DOFJzb0s6gODdX4m0LsG2rbWdgcVBE3sxWs7y+Wpt6VUR0dHR+eyI5JI88r5uSWdo9FW1IyiCcMTEyHu2dJelIeoqipnIi/x+NSXmU2Ossq2g9taP4lNdi/pGmtx2YrDZBT8i5wrVBXUqeMEf/0YE09GUVWBjruacb3zrQiVPAYaxdEBHduXfh6dNw26OFwEV0pL6VQojgpllUOAjR02+npO8UzsbxBHJ/AYOgmlZoglzahpJ3ZT/R/ALouBQExzJh3yaVWzXCi9q87KYVN2wTEZ5uMlcuQqh2aDhEESSGXmV7dcNdEoi2VxFs7IOUQlSdP0K5BeD3LBIHx0FuvZX+MOTuYfMqYCdEw/i6n9rZUvcvKYZtBSgJJME3rlNIGnDhM5OgQqNN21E2dvCsl/hLb33wNr79B+DimJ1SjTZDUCRi2SYrHYW7XYCVHS5hIrbQXLJq0tVcnOgeriUEdHR+eKRFVVDpzzFa1/K00smeG7Lw5zZjrMTetauHZN8SZrOO3jJ6P/HZehnfd0/ycGbLuWPa+wEJMs4rWtcPzEYpk5WXZ/sCCqAuOvw+nfoPgmmXy6HVOHg87ffRfG7mVq/5SM0H+9HkOl0xC6OFwE8eSV0VM6EYgD0O4sFocZNcXXz/8xs/ZzqIkW1gqf5t7Vb0MUJA7OaLuSVoOEzSRVDI4txWUxEE1msJtloskMGUXFn68c1icOc7uRFoNULg4L2jztJpm5rPAUBbAZZZg9B55+HIVxFqqCO3QaACNxzfFz1Y3ac4kQnH4Eo5pCFEEpeDtzYgbv1PPQXxIDkU7Ou48VcOH//IjIkfPIXifN77oW1w1bMHpt8NRfgL0dBubtui1GiY6l7noa7ZpBTPPa+cfcvVp+UiGiDJveqVUhM2lIx/UWUR0dHZ3LmHRGYXg2yuqW4s9qVVV5ftBXlNG30sxGknxj/xCz4ST37ehiZ59mYhJNBzgeeoadnrfhMDTzQM//pMOyDklY+HbSIAnIklC2xtdLh9u8ouJz0agqzJxu7Pjx1+H0r4mcnsW62oO054P0XdOLsaMZYTnzB/v26Wu/TsPo4nARXCltpePBOEZJxGMzklHTnI8eYrVtB5JgYL3jOvZ538uD+5tJeeyIglaBy8VHWIwSzXYTkUR0wfdxWgzMhBN5w5hwIk0gmsQoiUWRGLXwZEWkySABKVBV5HSEtMFelFFoKxCHFqOEqKa1wPd4AIe5h5msOHRER5DT2rXLogi+Qa1yZmuBU7+BlPacUZKIK8V/n9bYmOZI2rYZnB3agzMnSY5PEXz+GMEXT9L37z+AZDPjfdtevG+7BuumvnnH0dMPa22ie3+3yESmy2PBsJgPfVHSBKB3rRaoW7o4tm8rF4dtm+eNZCQZJH1x0NHR0bnUTAXjzISTFc3aTk+FOTjsZy6aZEevB0EQ8sLwvG/htXi5iCTSfOXZs8RTCh+7rp/VLXYyaopX5h7k2ZnvklRi9Fu34zX10G3dVPd596xqwmk28OjxyUVVQJfqUrpi+IfzXgN1ceJB0sceZ/JQG8HBZjrW3Im7eztLD/kowTugRW3p6DSILg4XwZXSVjoRiNPqlDgUeJj9vn/Bn5rk4/1/R5t5Ndc3fwCAV13nGfPPD7dHk9rPZjZIOMxyWatmKZIITrOMQRLzc3/heBp/LIXLaqhrl88ki3nnUHO2hdSQDuEKDzLjubooiqJw7tBhlrVqXiYJ46/hcVo5h1YldQfnB8MNUvYazu/XIhkSoaLnsh46BY+J+VyiTFom+No4gV8/RuzEMADWDT2kAxEkmxnblhL3z9gcnHkU2reXhcwuShgCDNwKru7qz9uaNdGYc0k1WDXBqKOjo6Nz2ZDKKOw/6yOZVhhotRU5Y2cUlRMT2vz4yYkwwViafQNeXjk/d1GFYVpR+PaB84TiaT51w2q6PRZOhw7w2NSXmUuNscq2g9tbP4XX1NPQeQdabPR5tQ3LG9a28OTJqYbmHwWh8ojMZcF0A0Y05/cTevpZxl/tJhNXab5vH67rtyzv9YiSdk/Qs3d5z6vzpkEXh4sgnqoullRV5dh4kOcHfdy6oZWBlktTsUllkkyqT2BqfZJfTczSbl7L/a2fodW0qui4TreZY+NBEqkMJoNELJnBbBCRRAGDJOKyGJgKVW9lcZg1AWiUxbxzaDiRwh9N1e1U2lQwQ5CzqDYn53BGhph1bSmqHBZGVtiNMkwd175RVZqnXkAU9mFOzmFMBfLHyTlRlormK4Y5jHK5YBOTKVLROAavg/T0FBN/8zWMXV5a3nsjrms3YWh2apXB4f1aK4maAVePVt0bek5rGdn0zrp+9gUxOcBZx+xB+9Z5cdi1Uw+41dHR0bnMODjsz49qHBsLcnXv/Nz5malwUbvleCDOz18bI92og8wSUFWVnx0cY8gX5X27euhpspJUYjw08beYJQfv7f7PDNh3NXxet9XAzr75n7XdZWbvai/PD/ryj5myWcaCICAKAqmMkvcuAGi2m2rHTF0q4kEIjtZ37NRxpr79K3zHvJh6PPR+5q2Y+9uW5zoEQbsH8fRr9yPS8sWH6Lz50O8gF0G1yuHZmTC/OTLBSNZm+vh48JKJw9lYBNH7S6xSD3d1/SGrbTsrVvFyzl/jgTj9zTZiqUy+FdQgCbistcVhznDGKInYs8ItFE/jjybpdLvqutZC97NcjIQpOYegpHFEhjDJ8+KosHLoTo4VVQEtQpK22RcRSobC85XDChiknHBMI58a+f/b++/4uO7zwPf/fM+Z3oEZ9E6CnRRJiSJFimpUcZMtK7EV2XFvcRLvJr/s3U3u3ru7N1vyc+7dvbvZ3TiO474ucrdl2ZZc1AtFiVQhKXYSJACi9+nt3D++6DMAByBIsDzv1wsvETNnzhwcUnPwnO9TsB88TdeJdnxbV1L/zx7AWVPOir/9JI7acn3+EkPw9Och2q1f5wzoD+GuN6Z2uvrtSzdkNtxaWiF5oFavIFqWtKsWQogrTNdIgpO90cnvj/eMsabaj8dhI5+3ONJV2HX6cgeGz5/sZ/+5IXavcTHq/iV56w9wGG4+2Pg3lDvqS6ornM1mKG5tjWAzZ96IbY54J8dolXsdRbuRn+yNcuDcENmcRW3oClo1zKb0yKnU6NwdzFNj0L5PN4ZzBbEsUG98C19rGLVqO5EHb0MtVbBb3gI1W8AdWpr9ieueBIeLkCgSHL7SNshPXusk4LLx4NY6XjjZz2AsveB9nx9OUOl3FnyQXkg6n+T14V9xOnaAP6j/a0ZiNmJn/oyHd9zECt/cAWptUAeH50cSNEe8xNPZyRRPm2FccPVvIji026bSSofiaWLpXOnNaKZtN5FW6kzrxjjB6Cmcttsmn/dNG1xfNnYSpsVNTruBJ9E9Y99KMe+5dNgMnE/sw7HvCCqdIe91EbhtI6Fbp+oonHXTRnqceQZivbDhQYis0UPsldL1BiPtEB+E+iWa7aRUQWrqvKo3ge0KuoAKIYQgnc3z8unBGY/l8nCwY4QdK8Kc6otOlnRcivdWatqN0GkGY2kOnx/h3GCcc4NxxpJJGpoPcMT2OJn+JC2eLdR71lPhbF70+6+vDczZtbyhfI6xUeNaK33UBF3sOzN45dQbdr0BnQfm32akA175MiSHySYNuvcHsblzVO9y4Xn3Z/AsRRBnOvRKYdWGpbsZLcQ4CQ4XodjK4cHOESp8Tj63pxW7aXCse2xy5l+pEukcX3j6JLesCHP/DbUlvSaVi3Ng+BfsG/wJ8dwIzZ7NJPJRukdSWJlyaoLzf6D6XTa8Thtdw8nJY5hI7bTbDDxFBs9PNxkcmjoN1WU36BhfOS01rbRsdlqpZeHMDOv9ZsZwJbrAq2v7JkZXOFODeGwDMG2QvU2pglEXNkNNjx/JJVLE3jhN9PXT1Hzq7ThMA8vjJHPDCjIbWmBlHRtWzDHfMZfWdwKrb4CWO2Y+5/DqofWLVdZU2FQmWA+O+S+eM/fRvPj3F0IIcUkc7BwpGvyd7o+xptrPW0VWDRcrmcnxmyM9dA0nGYilGEtmcdkN7lhdya6VYeymQSaX5+ljfTx7oo9c3qLMa6e6+iRu908YpoeV7m3sqfwkEWfjRR2LUrCiwntR+/A6bdy1tvKi9rFkBk9fODDsegNe/zaWzcOo4/fo+flr5FMZIve1wu494CpsRFQyZUCoAcpX6t8PjCswzVZcEyQ4XITUrJrDXN7i3ECcG5vKJu/OlXsdHO8Zw7Ksklsv90VT5C29CnnH6gr8rvmDq57kab5z7l+TzI+xwnsTt4Yfnuwc1j3aTtBtnwz05qKUojbo4vyIDuji6dzk4Hq7oQi5558pFPRMpZWCHko/ERxOPKdU8TF8ADZTEXDNXDm0Z8dQE7P5AMfAMYg0jW9v4HYYBAZO4IwU3gl12U0yuanX2kyDXDTB2KsnGNt/gtihNqxMDjPgId0zjCMSJH3b1HDY6WMzCpx/XdcsNt06zxlZBGXofdq90PvW1OORNUv7PkIIIS6rWCrLyd6xos9ZFjx1rHfRox1mS6RzfO3FM5wfTtBQ7mF1pZ+wz8G5wThPHO7mpVP9bG8pZ//ZIYbiGbY0hLhvfRUBt8lXzvwdHhy8u3JxdYXFVAWck5lIV72xHmh7fv5tTj0FR35GxtZI1/4aYof24m6tpebTb8dZF5n/tfNxeHUWUWT1wm4YC7FI18j/tZdPJpcvqAM4P5wgncvTEpm6Q1bmdZDNW4ylsjOCn/kMjK80ZnIWL5wc4O0bqwu2SeTGGEx3ko418NJpMF2bqE7fhoq2YPgjMP650T2SLJhvOJfakJvnTvSRzeVJZHJ4xgNKm2ngsBl4HGbRu56mAf7xVNKJoNjvsk2umE4ElpvqgrzZMVLweoAyz8zg02k3cI2nlOr3UJhjnZAcAVcQRruoH34VI9mBzQgV7M9lNxlLZDD6R7BsJvaaMlKdA3R9+XHskQChPVsI3Lwa9+o6PUsoO7s+0YB0HNr36k5f0z+Izz6vh8+Hl7imz1+j6xLqb9Ypq7F+Pc9wvg6lQgghrhj5vIVhFN4IPtg5Qm6e2G+pAsN4KstXXzxDz0iKP9zRxLqamStUZ/pjPH6oi98e6aXS7+RDu8o5zy9xO/8QQzl4qOGv8dnKF1VXOJeWyFU6Qik+qK/DrqBe6ctl4NSTkJ8n9TfWD0d/DlWbsGrfTfJn36PqQ3sou+/Gxc8t9JTrcpGyFhliLy4rCQ4XqFhK6Zl+Pd+mOTwVSITHUyUHo+mSg8P+aBqFztHfe2aA21dHJu+6xbMj7Bv8CfuHH8NpePD1/jvO9CcIed7PMNCeHOV4zxifuW0F5T4HvWNJ1lT7S3rf2pCbvAU9o6kZaaW28UYuE0Pup7NnRvAFI5OrovbJlUN9vAoIuG2YBmyoDXB+ODE5g3C6sll1iS67iSszFRxOHANtL+hVu9QY5ckoiVldRq1sjvjRdqyXj+N94zTm4CipnRuwvf8O3KtqaflPH8XZWFmwiju7DsOVj8Peb+rOn31HYfsf6dSNkXY9y2jDg0v/IR0aT90xDFhxJ7z1qG4qIxcDIYS4osVSWY52j3KmP87u1siMcQsjiczk7weXUjSV5avPn6E/muJDtzQVvfa3RLx89o6VdI9GOZN5gscHv0cmn6LJs4lV/lsI2pc2ddNmKhrKrpA6wVJZFnQfhPOvwfTGdvOlP41LPv8oo2/6qfjz38fhDtH63z6L4Vxgx1DTrvsGOP1QuV6nkAqxDCQ4XKBiYyzaBmJEfI4ZaaDl4ytig/E0zZSWcz8QSxHy2Ll7bRWHz+txGDtXOXh58MccGPoFGSvNOv9t3FL+EF88EufGxjIe2KI7eQ5EU3zp2dN87cU23rO5lrxF6SuH4xez0/1RLMAz0a10/G5X0GOnayQ5ub0n0UXV4CtYVQ9NPuawGSjFZMdSv8uGzTDGH1fsWBHm8UNdM+6gqnyWsiKH6MsNT/55cgxFtGfyMafdJG/p+kHT7cSyLE79yy+T6RsBm4nVUkNi10ayaxsJmQplGLiaZrWLtvLQdwwjUDdZp+jIjNJw6luQGYWW2+HMs3D4J7DpfTo4NR16dW8+7jLdnCZXYjMipXS94eQP54fm3eCZo+5RCCHEFeHVtkFO9kYn5/U9e6KPe9ZVTY5nerNj+EIxxUVLZ/N848U2BmIpPrKzmdbK4qt1lmVxIrqX3/V/heFMFyu9N3N35ScXPK+wmNtWRdh3ZnDGTOSGMs+CG+stq+QotD0H0d7C5+b5S8wn0/Q98gSDv+vDdAcoS5jY3SwsMAyvhKbd+gaxEFcACQ4XaPbKYd6yaBuIsalu5tiGkMeOggV1LB2Ipon4nFQHXayrCfDSqQHqa2PsG/wp6wO3syv8B0ScjZwdiJHOnp4xJiPsc/KxW5v5p+dO8519elh7KQNjA9HTWJ5GnDaDU326zbbbMZEqqleupjeWcaYGqe7fi7KyBPODwNTdRpuhJtNMJxrVTMwlCrrtbKoL8Xr7MABGPkNN3/NEqjcB0zptWRa+3CgT0winj6HIpzLEj3WQOXCS/KGznMnnWflfPo1Sish7d2L6PbCqnrf69asr/E7q5rpzeexXcPI3gGKNt4EB3yoqB/djyydgx2ehfAUoE04/pdtDd+7X8wPt8+T712yGuhv1hSQ+CGNd0H9cp8TOxVsJ9lnHOD1YFEIIccWJpbIc74nOeCybs3jqaC/3bqgik83TPpi4pMeQtyx+sL+d88MJPnRL05yB4YQDw7/Epuz8Qf2/Z4XvpiU5hpUVXhrKPSgFzx7vn3x8epnNFS81Bsd+CZmF/X1FXz9F9zd+S6ZvhFBrmsp//mnM8tIytibZ3dCwQwJDcUWR4HCBZgeHPaNJkpk8zeGZH4S28QHypQaHlmXRH02xoTHHE91foKzOxZGum+nubuKzK/6JkGOq/vBkXxRFYRewmqCbj+1s5isvnAFDEfE5L/i+ZaNHMHMpaoJu2saDKo/DnDECYiLQs2Vj1PS/gLJ0w5ey9HlgqkOnw2bgH+8mWmnXH7LTB9ivq/HTPhQnn06xNvoK4XCKwNgpsLZOpVAmR7Ab43cf83nM8ccHfrGPvh8+h5XJgc3E3lpL2Y0rIToAB75CaMsHIdhAzrJwj6ZojngIqAwc/D403jKzk+f5AzowrLsJvBWY7a/T2PM7MqabwRs+TaR8xfgBv1uvWB59TH/fPEcjGsMGLbdNvYdS4A3rr6oN0H8Cul7XK4oFfwHNhY8JIYS4op0fLh5IpLJ5njrae1kasfz2SA+Hz4/yjo3VBTWGALHsEM/1f4dbyn+fkKOa99T8b7hMH4Zami6XDpvB5oYQAPVlHlorfZzsjeJ1mlQFLvz7xxUhm4YTv1lwYJhPpjn/pV9huk2a9vTjuf0+KF/ESIn6m3XPASGuIBIcLtDstNKJeoJid8nKvI6Sg8PzsfMQ+SEnnftRw4qbyu5nVaWPF04NsLt1ZtfKU70xakPuohefxrCXT9zaQt9YanLA7FxcyT5s2ThlY0epC26jbUD/LG67iW3itdkUwWw/oegJAmOnMHNT6aWBVJdeJRsP4BymQUTpVbJaNYg9M4rTNhXUKqW4tzWAcfLX4E4AdkhHdS1fWRNWPk/qzVcxnjuI+2g7trYuzD//faj04ayPUHbPVrybWjBW1pI1DV3LefjHeoXu5O/gpo9hKsXG2iCGAk48pRvLdLwC698DzbfDaCe8/l1d4L35A2DY6ArfxnB/DznTSXOwYuoEKQO2fgRe/O+6W1hwVvqNUuCvhfptc88ZUgoqVuu0kfOvQ/ebM58PXVyrcCGEEJdfxxzBIUAslSOWuvi5hdlcHtNQRTuev94+xNPH+tjWVMbu1pmdMLP5DK8O/YwXBh4hm09T515HyFGNxxYs2M9cbKbCUIp0du6GOZvrg7jsU4HmjY0heseS1Jd5Su7Svqzyed1oZr7snmmsXJ6R5w8T3L0Bw+Wg8a8ewtn2HVTKpUtRFirYoH83EOIKI8HhAiWzMz/wz/THCHnslDstyGfIG9PqDr0OjncXb2E93YGhX/Drni9iDypaHHt4e8MHCdorOc4YX3+xjbe6RrmhPgTo+oL2wTi3ts5dk9YU9tIUvnBKhz+u5+qpfJbV9j5eQB+722HiUFk49jiMdWED6sZGZqyaGga4SOv8fL+u57ObBtX5bsBP2JGhYug1nPXvmnrD2ADGqSd1QIj+oLUyWYzeIyQ6xmj/9KfJDQ8DYJb5yWxoxj/eHMe3eQW+zXpFzwLSuby+03fuZTDsOuhKDIM7pAPDXFa3nQ636gLvwz+BgdMwck4Hets+oVf80EFt2hEa/xlmXdDsLrjtX8wsTncF9EzDspbS20obJtTfpP97/jX9mDcCzqu0m5sQQlynsrk8vaPJC2+4QIc6R9h7eoDRZJaxZIZUNo/LbhDxOYn4nNgMxUAszUA0xWgyS0vEy3u21M4IxI6PvcTver/McKabVt929lR+irCjbkHHYTMUd66poHc0NWen8XKvvSCN1WYa3LoyUrRr6xXp7Av65nIJ4sc66P76b0i192G4HQS2r8FltMFoG2x6v+5JsBCmXWc1CXEFkuBwgaYHSPbUEOf6RtgSjNPS+SgDoRsYDkyt8oW9DsZSWdLZPI5Z3TX7U+cwlZ0yexWrVJgj5r28deJG7r7rZoJ2nWLQWukj5LHzatvQZHDYNhAjZ1kz6g0XQ1k5fPGOye/XmZ1AMwB+I019z/NgTX3Aux3mjJ/dbTP1cPnhs5PBoZMUrUY3u8vz3BiM4k5m8MfOAuXQd5zc8WdInugkfryDxPFOEic7Cb/7FiIPOHA0rMN31114Gl3EKlx0WOPD7ousyCrAaRpwdi/kUnDTx2H/1/UH/drxYLTrAKRGYcsH9bzA00/p9FBlwq5/rhu/jLNP+7uZ3b0UGB80Oy0Np+lW8BeOGSlJ7RYdaHa9ASGpLRRCiCtVf1R38G4on3kTsGskOe94isU42RvlkVfOUeZxUBN0sarKh9dhMpbMMhBN0zYQI5OziHgdtFb6qfQ7ubm5fKpp27jTsQPYlJOHG/4jLd6tCz4Om6G4Y00FlX4XIbeDo91jBauHSsG25vKiq4Nl3gUGSZdTtE9nKsX6ID5QUuO47EiM3keeYeS5Q9jCfur+7L34t62CoTZ48/t6IH3DAoM8m1MHhnJzWFyhJDhcoNS0tNJM12HGMkHWe0cB8CY6ZwSHEx+Sg/H0ZOfQ3mQbLw48wpGx59kY2MP7wx+hMmdQkX4IleubMffPUIptTeX89kgPA9EUYZ+TU31RTEOVtDI4H0+iCyOfmfy+zpXEpiyylmLV0LM4HRlgqobB4zAZmlYy5x3vSsrQWWjYDkAgfo6kyvHPWrogb2H0DuON/wq8MayeY5z4k/+BlcqAAmdDBcHdG3Gv0nc0zUQHtX/zn+C1b5Ebi9P0xg8oHz1CfuQGaLxZN2lR0y6C+ZzuJlq+UjeCqdoI516EVffpFcHTz4KvWgeGSsHKPRBZpV83qz20w5weHF7gjqfTv/jAcMJE0xqpNxRCiCvSUCzN08f6cNoM6svcMwKhueoNF6tnNMl39p0l4nPy2TtWzkjVvJBYdohn+v4XNwTvod6znj2Vn8CmHIuqKzQNuG11hKrx31ccNoM1VX4Ods5cPVxb7S+pp8EV5+zzOsNoATr+7qckTnURfvcOIg/sxHA5IDEEr35Fz0Hc9onxG8glMB26D0HlerBdwUG0uO5JcLhAEw2NXal+3hpIAEHW+XQjF1d6EDOXJGfqD9aJcRZDsTQ4Onmh/7scj76Ew3Czs/x9bC9/EHesA0+yl/5YNWUeR0Gd4E1NZTx5tIf9Z/p4cKXFqd4kjeWegpXIUtlMhctu4us7N/NxBY3uJJ1JJy4rgWnM/KfhnnaxCrhtNE7cSU1H9fBXbwTPy7/Dev0tzM5+zM4+VCpDsqUaNtSgTIOqD+3BXu7H3VqL6Z3VSXXwtE4BzWex5xOUDR0gbQvgPL8POl7UIyI2vh+q1uvtuw/qD+gND+rvW26HnoO62YwnDKMdsOkPZs4KnF0zOG4iOJzehIfKddB7pHDjpaoPqF+aTnFCCCGW1kg8w5NHe0ln86Szec4OxGmelsVyfmRxwWHesoimsuTzFgG3HUMpxpIZvvlSGzbD4KO7mksODLP5NK8M/YwXB75HNp+hyrWCes96HMbiZwtubSyjJjjz9Wuq/RztHiWT07/9lHnsbB7PZLqqRPtKDgxjR87haqrC9Dip+vDdGE47ztrxUp5sCl75il51vOVPdZnKXJShf3fxRsAT0Te5pfmMuApIcLhIZaNHOBJ1E7JlqXaOr8BZFt7EeUZ9ujYuPL5yOBBL02k8ydn4m9wa/gA3lz+A29RpjZ7ka5i5BINjCcK+wjtJDdlzbClL8VpbHx/wnKFrpJW3rVpgq+RpVlb4SKfiqGR3wXOb/PHJP5uzUlUmgsOA3aApmyL6UgfJs71kh6LU/fsbAAXP7MNxuI18VRmZG1aSq6+kcWvL1Dm7a/PcB5bPQvvLALi6D2BYOU40vp+VzU14Bo7oovFX/gnWvANa74Uzz+ggsGqjfn24Ffw1cOY5PXrC7i05AJsItG2G0qmyhqk7mSaGC+sRwq0l7VMIIcTVZyyZ4cljPTNm9h06P0JTWDdZGYimSKRLzylNZ/N8/9V2OocTjCUzkzMRbeMdxdO5PNFUlk/ftmJG5tB8Tozt5be9X2I408Mq3w72VH6S8gXWFc7mdZq0FilXcdgMVlf5OXx+FNOAXVdTTeF0/ccvuEmmf4Se7zzN2L5jRB7cRcXv78YdtuD049AR04FhcliPqrr50/NnEUVWSSdScdWS4HARnOkh3PFujo6tZJ0/PmNxaiI47Egc4YX+7+Ly38hgLMy9LQ+zO/IBXObUh6/KZ3GlB7As6I+laYrMDPps2TgVQwe4r9zL5wcb+GZ7JRaKXeZhwkOjDIY2Yi0gdcRQepxE54lTDFqFndQerutjYqy9gUVmKEqqox/v+kZcdpPASwdRv9nP2YweZaFsJs6GCPneExj5DJ4P7KEjqUdNTHDVFrbXntR/QncJq9+mv48PgGVh79xL1F1L3FWN3eHWz9fcAG9+T88i6jsKQ2f0quFEqqlSuhvpwe/pVcPWe0suELcZCtNQU6uGgTpdLF6zeWZw6K+eUasohBDi2pHN5Xn2eH9B8DeayHJuME5T2EvnAlNKXzzVP95ULkiZx0HQbUcpPde4P5piLJnl/k011JeV2NwMGEh3Yjdci6orNA2K1kturAvOGfStqfZzrGeMzfUhgp4FDHe/UuSy+neGOeRTGQYee5mBx/aBgsjv30r4HTfrm9BHfq5/H/CEwXTqcpU175zKYprNFYDGXRCouUQ/jBCXngSHi1A2coR4zmAgY2elZ2jGc+2xN/hB4pe0xd/AbQbweTYwFEtPrhRO50r1g5VnOGuSzlGwcuiL69TPzYEYYXuGF4aCuIwcKz0JbGPHMawMfeXbSj7u5ogXj82gPNnO4MSDE1cJ08DW2Yfj1SMYfcNk+kY4mUgBsOJvP4mzLkzdunoS5HE1VuJsrMBZG0bZTMgnoP8E9pAX+mfO8rMVa/ACOhB85cs6NcNTrofOAwydQUV76Kt998w0T9MBWz6kU0OPPKo7kDbsmLnP+pvg6M/13b3m3SWfF9CNaCbrDcvHVzsDNeCr0rMOAcKrFrRPIYQQV499ZwYZSWSKPneoc5TGcs+C6g3jqSzPHO9jXbWfh29e/NiiaHaQZ/v+F03ezWwI3MnN5Q+wvfy9i6orvGttJa+dG2YgOtWMxe+ysWKeofUuu8muleEFBbBXlKE2yBX/ewXo/sZvGXn2IIFb1lL50C7snhy88VV9I7pyA2x+uLQbw4FaaL2n9BpEIa5QEhwukC01jCfRydm0ThWIOKc+cP4x9TSv5NrwGQH2VHySrWXv5AeDPfSMpmbs40TPGPVlHsKpPgC6kjoonF3g7Y/p4NBQcFdkmB92VbDen8A2HsMEomeIu2qIeWamk9gzowSjpxnyryZn0x/mSsGabA+D//X/IXm2A3dbL8bAKMbwGPEP3UeutR5jLI7tyDnylSFsN60mvKISZ30Ee0Sv/gW2riSwdaXutjncDtPjvny26FxFW7E7kVYeXv+2PihXEN58BG77l/ru3LmXwOZktHzjVJrnBKVgxZ16hISV0wHidKYDNvweZOJ6vwvgsCndqdQwZ9Ym1tygB+QaNl0vIIQQ4ppzomeMtoH4nM+PJDIc6xljMDZ3kDHbM8f7SGfz3LthcU3MdF3hT3lx4Ptk85nJ1FFTLe5Xt+awh0q/iztWV/DE4e7JWYw31AcvOJfwqg0MoWhKaeLkecyAB0dliMhdTYSCB/EEX4R9T+oNDDtsfJ/uTl7KzEa7R/c+kMBQXAOWNThUSr0d+Dv0nIAvW5b1+VnPq/Hn3wnEgY9ZlnWglNdeKv6Ro+SAgYwNsBiznyZrhbEpgw1mHSuNSrYG72E4fCugZx0e7R7DSI+Rd/jpHE7wtRfb2N5czudqewHoTungsMqZAvTdKUd6BEdmqkPYnsgIj3aHuTEYnXE8lQOv0m74yboCGPEYFc//krKOA9iGRqgZjsJIgu4HP0Tk9rW43txP2z/9DOW0Y5T5yVeVkV3fhBXQdwyzaxuJ/u8f0sdd5qY8NKuwPZfWA+VPP61bQdfeBFs/NPnBOTs4NNQcweHpp3UDms0f1Hfj9v0jnPg1rLhLD4qvvxnT4UJZhS8F5g/S6ktfSZ3OYRrYTDWVUjohWK+LyV2hmY8LIYS4JgxEU+w/O3TB7V4/N1zyPkcSGV46PcDWxtBkt/KFOBV9lSd6vsBIpofVvp3cVfkJyh21C97PBJuh2NwQAvRK4B2rK/j1Wz34nLaL7n6+rPLj2U/GHFlKyZGp7B8g3TdC3/eeYXTvUYJ3bKL2Y3fhaP8xjvI01GwHlx+cAd0J3Rsp7RiU0oGhffHNgIS4kixbcKiUMoG/B+4FOoBXlFKPWpb11rTN3gGsGv/aAfwDsKPE114StkyMrGXxZq4dT/PP+L7RQTB3OztsK9ht02mH+WQ/w1YelEHYYyOXt3B1v0q88S6eO6FXC19rHyIbGsFpQnfSgYlFnRoiSgSyWYLdhzF6hlDxJJbbSbi6nL9ff5yKX7+AEY2jogmMsThqLI7trjc58eF/gyM5RO23v4xlGlgBL/mQj/yKKspUG62WC1d9hNb/8SfYQl4Od42RSGVn/nDT7o7NCPRSUd0Cuu153Z002KBTOttf1h+ea96hz82sQNBWbCzEaBcc+wVU36CLtZXS/z31O73vfAaadmJPGuTyc0WHS89hMzEUUyml01XfsPABt0IIIS6pkUQGu6nwOBb/q8xgLM1zJ/op5XKzkEvS7470YAF3r6ta0PFYloVSikw+idPw8IGGv6HZO08ztyJqgi76o6nJDqOg6wa9zqnzFPI42N0aIW9dvuvskrMsXReoDFhxR/Ft+k8CkIslGfj5yww+8SooRfiBnYTftR0OPgKJQdj5uanyloWq2SI1huKaspwrh9uBk5ZlnQZQSj0CPABMD/AeAL5pWZYF7FVKhZRSNehp7Rd67ZLLW3le73uDXw++QFd+mMp0kPdlt3KTIwxlehujZxAzmSbc/wwZy8XO9sOc7gszNGZH9Z1kxTO/5l4zSWJkjOShYSIqyir3SipX34MvHaXpz/8vHEP9M943feNqkg/eTsBpYT96FsvtxPK7yTZVYwU8qHo/kaHX8cXbGftXH8TyuvWy3biAy4Z//KJgL9MNcdx2k1gygy/RQcxdW9DYxjCUbv185ilof0UHbZXr9bzA8vFxDpYFJ57QAWL9zZizgkE7OXjrZzBwChwecPhg+CzY3LDpoalgdP179diIcy/pwDPYgD0Tw5hz6XAJGKZeqezYB8lR7KZCGbbi4y4knVQIIZZUMpNb0Dy/6fJ5i7e6RjnUOULegpDHTk3QxcpKHwFX6RkeJ3rGOHBuaEkH2luWRcdQgv1nh9i1MlxyB9JodpBn+r5JmaOWXeGHWOO/ldX+nQuuK7Sbip0rw2TzFs+f6GcwlsZlN1hfpDlc7ezsoKtN+z5dTwh6oHzdrA7luQwM6OBw4Od7GXhsH8HdG6h4/+3Yw344+yKcfw3WvmvxgWGgVjevE+IaspzBYR3QPu37DvTq4IW2qSvxtZdExdd+xb9pn6g5GAQGydW1EfvsAwC4f/gMZvcAXh6bfE0+spKz297LmRPdPHT6BapTw8RMJwmbA8OvyJKhxpXBnepj4Pa3Ycsn8VldWB4nlsdFvmy8EFopov/qg0WPKzh2AgDLX1gXEHAXXiw9DpOagZdo7Pkt5yr30FUxs4GLPTUML/9nwIK6bbrWb3bb5hse0rMG33gEHD5s5aun9p/oYtX5n0CyXweT6ThEe3Wt4JYP6g/yCQ6vzu0/8HVo2qXf3zRQqsQrttMPqbHStp1Qtw1CDboD2fFf4YinyfrrJHVUCCEusVgqy+vtw9zaWmLa3jQjcZ2uORibaqgyHM8wHM9wsjfKra2RCwY9qWyO/W1D89YYLsRQPM1LpwZoH4rTPZIklc3jtBncsabygq/N5FPsG/wJLw38gJyVZVdY9wxXSqFYePC8riYwGXTft76K19qHCLoduqb+WtL1JvS+NfN7hx8qVoNlYfUeY/RH38Je5sKzqo7yd20ncMs6XM3jK7mj5+HwT6BiDay8e/HHMZEBJcQ1ZDmDw2L/N81eKpprm1Jeq3eg1GeAzwA0Ni6+WxiAoQyCb3s7sb4hHuuJkFfw3pohrGkD3ZP374RMFkwTy26SM03+35Pr2RKDZwb8RD/4L3jX7hvpOPgsXzhZxl+1tvOFU3Xc4xzGyKcZuv/dBKKncUZPX9SxTjc9lWRCYPANvD2/xUIRHj1cEBy6+t7Qq4V3/NXcs3wMG9z0cXjxv8G+f8Tm9LPKWU/a5qNycD95uxd2/LH+8L2Q2i3g+1eT76U7h067mHkrdJ3jbMqAljvg6GOFz80l1DDVhtrhgdXvwPbmY1iRRd45FEIIUbJTfVHaB+Mk0jncjtIDoEQ6x6/f6p6RLjldJmfxzPE+tjaGWFutV8qGYmmO94zRMZQgm88v6SrhcDzN08f72N+m6xXry9xsaQhRHXSxqtKPr8i1d7q22Bv8suu/MZLtLagrVAo21gY50jVKtsR8VpfdYG31VFdNw1Dc1FS+yJ/uCtZ/Ejr3Fz5+7iXIpYk++Th933yMZFsPwd0b8Kyqw+b3YJu4eR4f0HOT7W7dBV0tMnAO1Opu60JcY5YzOOwApufw1QPnS9zGUcJrAbAs60vAlwC2bdt20XmKvs1bSA138auDK2j1Jsis0HPwqgb2EYyd5njjH8y4i6QA+r082W8nh+LBijZsiRpu9ffwHZufRzorSFsG1U59F9Sd7MEX77jYw5zBO/vi23cMz1vfZ9TTxLCvlcbe3+FK9ZN0Tt3FdfS8DqGm+Ye8gg6udv0ZdL2BGjyNt/ck5ZlhBgLria5+kKaKitIPNDBVbG83DSzGr+JOP6x+Oxz5GSRHZ76mvAV8FfoDOj7IBTm80DRrzIXDg23d21HSvFcIIS4py7I43Rcjb+kgcWNd6Z2l958dmjMwnNo/HDirRzUk0jl6x1Lzbr9Yr7cP8aMDnWDBTc1l3Lm6glCJKaSWlUcpA4fhxmX6eVfNn9M0q65we0s5Kyt8+Fw2Xjo1UNJ+N9QG5x4fda0Y7YKzLxR9KnH6PL1/813ih89ijwSo/ey7COxaN3OjWD/s/Xs98mrHH1/c7OLKOWYdCnGVW87fhl8BVimlWoBO4GFgds7ko8DnxmsKdwAjlmV1KaX6SnjtJZO3dLfSWxy6oUt4+CDN3Y8D4El2E3fPLEyucmboSzu4MRilwZ3G6t+LMuCeimF+2KUDshqXDg7LRo9h5NMUY+RS5A3HglIYXHZzZjrJaCfs/yrKV8mphochm6Kx93eUj77F+Yrb9WtS/Zhj53UtYCkcXp0O2rSLw+eGyGX0cdY6F1/PYDcVljV+3I07wbTpou8zz87csHKD/m+woXhw6ArqD/98To/QqN0K9sLOcS63/+ouzBdCXDdK6PT9h8Bfjn8bBf7Ysqw3Lu9RFnd+JEk8rUconOyNsqE2cMExCgDnhxOcGyw9DfTsEqWMFpPLWzxxuIfqgIsP7mgsua5wLDPAM33fwFA23lnzz6l1r+bjzX9X8PNvbyljZYUuvWiJeOmPpjjREy22y0lep8mqSt+821z1EsNw6kl9LS/29LEOUud6qfrQHkJ3b8Gwz/oVN9YHL/297rx+y5/obuSL5QroLCQhrkHLFhxalpVVSn0OeAJ9gfuqZVmHlVKfHX/+i8Av0WMsTqJHWXx8vtdermMfzZpkLYOwI0MgdoYV539G1F2HL9FJKHqyaHB4aAzeU6Xv/ilLB5V3R4b5TZebiBqmenxe4lyBYXDsJKvbv0/CGeZ8ZDeDgXUzUiEiPif90cI7pF7ntFXDbAr2fx1MF2z/IxzDBtGkkzFPA+GRqeAwPHIYC4Wq2bLgc2MzDTI5x/ifL3DBV0rPBkrHiu4nb+V1M5jg+BzH8hXQ9YZuTQ16VdMb1n8ONujnZmu5Y2qbeRiGwiiarSyEEFeOErt1nwHusCxrSCn1DnT2zGWpy58unc3jsM1cyTrVOxXkxNM5OoYSNJTPP0Mvm8vzSlsJmSGXyaHOEUYSGR7YXFtSYDi9rjBPlu1lD052JZ0eGNpMxZaGEK2VM1ezbmosYzCWnjG4frZNdUHdSO5alUnAyd/qwG5cunuI/p++iGd9E6HbNxK6ewvBOzZhup2Frx/rhr3/APks7PxTPbaqVPXb9Jit/LQO77JqKK5hy5pHZ1nWL9EB4PTHvjjtzxbwp6W+9nLpT+umJatUB6vOfZ+kI8zRpg+yru1bBKOnOF9x24zt90SGCTsyrPUlZjxe7sjyX73fYFf2ZQ6rPyVLANNQBSMcdGD4PZKOMEY+y6qOH5FwhOkO38JgYC2Ww0dT2MNoIoVr9AzVAy9j5lKcaHg/Xse0i+6Rn+s7Z7f8KbhDuGMxosksA4ENNHc/jivVR9IR0cFh+QqUO7TgczN9BEbRGYfTlbVA4y3Q9hwMt894ym4q8tj1yIwJSumuYBOrh9M/nH0VOtDMTLtb7K0oKTAUQoiryAU7fVuW9eK07feiSy8um3ze4pW2QbpHk9yzrmqy7j2RznF+eOZ18GRv9ILB4aHzo5MD2y+n0USGvmhqchUPdFrs8yf7ificrK6+cEpiR/wtfnb+/2Y028ca3y7uqvwEZY6pG8huh0FT2EtdyE2Fz1k0wDMMxe7WCI8f6iaVLVw1qwm6WFFxDa8aZtNw8neTjecy/SP0/eRFRp47hLKZOBt0+Ypht8Hs1UKA/hPw6ld0w7mdfzqjhOWCKtZC9SZwl+ljsPJ6tFV41VL8ZEJckaTIahEG0jaqGOQ9A18lb9g51vgBcqabYd9KavtfwMwlyZlTqYut3iSt3mTBflQ+y23WK9hVhvr+52mrfSc+p42RRGZym4nAMOGs4EjTh8mZTspHj1Lb/zwtXb+guesXpPyN2JJrWd95EGe8i4zpwcynWdv2v8hX/zHg0l29zj6vu45G9IfaRCOAwcA6mrofJzzyFkOBNbjT/eRr71rUuTFn3AWdp/ZBKd2ExuaE1nug+5AuMB9PF7EbBrmaG3XK6nQTq4dWHkKzGgwF66H/+NT3lbNqDYQQ4uq30G7dnwR+VeyJpWzYNiGZyfHciX76xmv9fne0l3vXVeF2mJzqixbMCuwaSTKazBSMoMjnLcZSWYbjaY52zao1vwyy+Txff7GN7tEkH76liXU1usFN20CczuEED2ypxZgnHTZnZTGVjYC9goA9wv01f0GT94YZ2ygFt6+qIOwrstI1i9dp47bVEZ480jvjHDptBresuApvgqZj0PEK1N6oUzTnMnwOzr40eeN34LGX6f3BcyilKLtnK+F33zI5oquozv3wxnfAE4Htf7SwBjJ2z9R4jGC9HnR/5hndZM+UX5/FtUv+dS9CLJnmm47/jtOKc6Tpo6QdIQBGfCup63+eQOwMQ4ELByah6Ans+RQxZxUVwwc4H7kVT7CKeDpLJmcRiJ6aGRjadA3fYHA9g4F1eFI9lI0eozZ5Ck78GtNTxena++kPbiIQP8fqc4+gXvsS3PgxPW7CXw1r3jX5/u7xdtcZu58xTyPlo29hWFksFMZC5vYoQ1/l8rnSVw7DrboecEL1Rv3hm03pLqiGictZ5IIxsXqYTRXWXoYapoJDuxvKmkv/GYQQ4uqwkG7dd6GDw93Fnl/qhm0T3USnr/JFk1l+d7SHe9ZVcbq/sIQA4ERPlJUVXnrHUvSOphiIpYincyxnGfhTR/voHk0Sctv5/qvt/OldrUR8Tp4/2Y/HYbK1oazo68Yy/Tzd902i2X4ebvhPBOwV/OPdX+etrtEZ4zcAVlf5SwoMJ1T6XexYEZ7RoOaWleEFdXy9IsT69SpcJq5HSqy4q3CIfDYF5/bC4GkyA6MYbiemx4mjppzQHZuIPLATe3ieoBLg1FO6kV35Stj2Sd1Aby5KUfAPrnEH2KalDZe36NRSvwy8F9c2CQ4XSOXSPDTyVRpUN8cbPjCjvjDqqSdrOAlFT5UUHEZGDpG2eTnR+BA3nPwCtf3Pkat+Px6HjdTweVa1/5CEIzIjMJw6EEXcVY0tVI9R8yBk4liGk/72ESxLB6pnV3yQljPfhWf+Vr9m+2dmzPHzTLugDAY20Nz9KxyZMUb9Kwk6S0xRsXv0amTfURg8PSM4tJtKv19kDfQcmnbsRvGhsaWmsZav0A1mZvPX6sAyn9Wro8ZVdsEUQogLK6XTN0qpG4AvA++wLKu0dpcXKZbOFk3/HE1k+dWhLhLp4o1EjnWPcax7gbNqL6HOoQTPHO9la0OIe9ZX8fdPneRbe8/y0LYGjnaNcueaioJaykw+ycuDP2HvwA/Ik+PmsveSJ0e130tj2EPY55iRFup1mmyuL71T64SWiJexZIZDnaOsrvJRd7UNsh88DW0vTNXvZVNw4te6xCRYDyMdMNIOo12kewYY+Plehp85SOSBnVT83q34b1qF/6YSUjrbnteBYc0WPa5ivpU+0w7r3q07oXYfhHRUZyYVu8EckXRSce2T4HAhclmaj30Zf+4M/yd/zAO+mQN8LWUy6m0hGD2p70DNk3Ji5pKExo7TU7aNlKOM3rIbqRzcz2DqXvyGneazj5A3bBxvergwMJymMjCevmr3YEennkST+kPXqlgL4U/C/q/BmncUdOZymMZkjaNOLf0VtnyS4bKNFFyyTDt4K3XN4kRBuL9aN3xxeCCbKAgObYah37PhZp0+OjGXKLL64tpHK1X8g9606Tt6ox26TkAIIa49F+z0rZRqBH4MfNiyrOOFu7j85goMrzTZXJ4fHmjH57Rx/w21uB0mD9/cyNdeOMM/PXcaw1AFaZw9ydP8sOPfM5rtY63/Vu6s+PhkXeHmBn019Tpt7F4V4cmjvViWHlWx2LETN9SHAD264qqRy0DHq/pG8mxWHs5Olcmmuwfp//nLjDx/GKUUoTtvIHT7xtLfq3M/HPoRVG2ArR++8I3iupt0JpMrqH8/6T8unUjFdU2Cw4X43V8THDrI36kP8aZ9Kw/QXrDJsK+V8rGjuNL9JJ26SNqZGsSb7GIwuGFyu/LRIxhWjoGg/sA7H9lN5dAB/Gd/QzA1jJkd5UjzR0nb5/7wd9iMgk5pQbd9Mjj0Ok3wr4O3/Y1eUSvC4zAZS2bJ2H2MeZrwJTqIhmatenojOgh0BXTQGx+E5LBuKGOMX9wC9WCYk6mkhhpPK52481Zzgy7i7nhF//lSCTXoY5pdqyiEENeAEjt9/1sgDHxhvBtm1rKsbct1zFeydDbP2YEYKF0zf/j8KD2jKT6ys2kyXbO10sd9G6p54nA3NzWW4R+vj0znkzgMF2WOGiqcTby79l/Q6Nk0ue+6MjeV/qn+A1UBF1saQgzF0tQEL27FbyJAvCpMzCZMlbY63PPtp4gdOkvZ3VsJ378de/kCbib3HIbXvw3hlbqk5kKBoa9qZn8Cw4BKubksrm8SHC7E9s/Q3t3L10/cy2ZP8dqJEd9KAEJjp+h2VuDIjLK+7Rs4smO0ZWP0hLcDEB45RNJRTsytu2Zl7H56yrZR0/MyACfrHiTqmb/BXKXfyeyyvqDbTueQ7gbnc46nkM4RGIJuSjM2Hky21bwdZ3oYZZ920aq+Qc8GnAgCldIdQGd3ATVtEKjFNnJCf2so/b6BaT9D5VpdV3ApA7dgAxSrVRRCiGtECZ2+PwV86nIf19Umm8/z1RfOFMxPvLGxjLXVM68jt6+KEPLYWVXpYzTTzzN93+B88jifavmfOAw3DzX89YztlYItRQK4dTUBMrmrYxX1oiVHdJpm/4l5N4sf62DgF/uo+tAeHJUhqj60B8PlwBYs4XeFkQ7ofhPiA/prpF2Pqdj2qRllNEUZNmi+dQE/kBDXBwkOFyLUQE/VHQy/ZSPsyBbdJO0IEndWEIyepK9sK6vPfhczn2LE20xT9xOkHCFirhoCsTN0Vtw+I/W0K3IrVbGjqKadDDtvoKCt2yzFCtl9ThumobCwJhvOFFDGZFfQ6dskXFUkXFUEJyLOirVQf9O8xzBDqBmz4xQw3qk0WF+Y/um6xGkwDs/8RedCCCEE8MuDXZwbjPPuzbXUBFzkLAuloKm8MChRSrG+1sXLg99n78APyZNne9l7yVt5io30bYl4CXqKByf2RaaTXjXGenSfgZH2wiYv46y8RfT1Uwz8/GUSJzox/W7S5wdwVIZwVBVv9lMgPgAv/U9dt+guA08YGnfB6reB3XXh19duufS/kwhxFZLgcIEGUwoLRdiRmXObEd9KqgZfYVX793Gn+jjW9AGingbWnfkGrR0/YjCwHgUMBDfNeF3G7iN317/BbrPh7hqdTA8txm4qXLbCC4xCrx6mc/mCVUVA1/qteYceB9F/AmeRAHJyHIWvcs73LyrUgDF+0bNPTykVQgghriAHzg6x9/Qgu1sj7CxhFMRQuovvnPvfx+sKd3NXxccJOaqLbms3FTcsotnMNWHg1NQs4jlYuTxn/u03SZ3txR4JUPWRewjdsQnDeYGVvunyOTjwTf3nPf+nDgwXwl0GVQuoYxTiOiLB4QL1p3TwE7bPHbiN+FZSM7CXYOwMp2vfzeh4qunxxofZcOYrVAy/QdRVS9JZ+GFms+m/Eq/DnDc49MxuXT2tDXPQbSeenuO1VRt0WmfTLqhcj/3kC9A9s0B8sqnMQj9sbU4MfxV0j2LY7DrFUwghhLiCdA4n+OnrnayIeHnbhuIB3oR4bhSPGSBor6TJcwM3hO6j0TN/UHFTUxkex3X461V8cEZjmemyYwmir50kdPsmlGkQ2LEW+ztvJrBjLcq2iM7ixx+H4bNw40cW/rsKQMOOeZsGCnE9uw4/vS5OX1IHh5F5Vg5HPU3EnZUMBNbTV7Z18vGM3c+xxg+w9uy36QnfXPA6m6kmB1jpC0tqzvfwTr/D5g7Byj1gc0NqDH9smPyZ14BZxd82J4RXzXido/kWOFokODTti0q3MMqbgRPk/bUyJFYIIcQVJZHO8e2Xz+J12nh4e+OMDtvTjWb6eLrv65yK7uezK/8Jt+nn/tq/uOD+G8rdrKgocRTUtSSbhlNPTo2oGJc6P8DgE/sZee4QVjqLu7UWZ22YyHtuWfx79R+Hk7+Fhlug9saFvz7UUDhXUQgxSX57X6CBpL6QzA4Oc6aLhLMCX7wdm93OmY2fK7ryl3BV8drq/1/RO1Y2YypN1Oss/KvJmW4UeYxcamrlsLwFmnZPBWK2MG5vGNNTDscfm/lBHVlTELA5/GGUzYmVnQpETUPpO3GLuKtmljUBYJW1LPi1QgghxKViWRY/fb2T0USGP7p9Jb4i19l0PsnLAz9i7+CPsMizo/xBTFXar0ouu8HNzeVLfdhXh7bnZnQjzfSP0PW13xB74zTKbhLYtZ7yt2/DWbuIVT7QmVHRHhg4qeci+ipgw4Pzv8bu0TfFE0NTjykD6gtvzgshpkhwuED9KQOvmcNlziyyHvavprWugvqRUTx2k3gmx8GOkeI7mSPosk2ranfbTQw1sydNV2QnKUc5zvQgGxuz4PZC1fqi+3L4ynSX0Y5Xxt/TmNmueRrLVwnDU2M5DKXAEym67YXY3X6SzgoCwfk7rQohhBCX04FzwxzsHOG+9VU0lBc2LovnRvnqmX/GWLafdf7bubPiY4QcVSXvf3tLOa65GsFdy86/DsPnyCVSZPpHcTVUYPrcZPqGifzerZTdvaW0zqOz5TJ6LmLXG/q/6ah+3BPWYypshU35Zmi8Rc8+PvOM7moK+vcgaUIjxLwkOFyg/qRR0IwmbzgY8a2gpaUS++HXAHDZzOllgCWxTUtvMRS47CbxdA6AMW8zqfEaxbwngre5hHq+qg0w1KYH15e3zNnFU/mrsaYFh6ZB4aiKEtlNRV/ZVhqcF/jQFkIIIS6RwVgan9OGY7xx20A0xc/fPE9LxMvtqytmbDua6SNgr8BjBtgU3MMK7zYaPBuK7bYom6HYUBegvuw67JTdd4zUK79l6LevMfLcIWxhPys+/wkMl4MVf/tJ1IUykKy8rlUcO69X+DIJ/ZUcht6jkEvpFcDK9RBu1V+lZDaFGmE8k4nWe6B9HwyehpotS/FTC3FNk+BwgfqSBhHHzFrAYf8qPG4XdrdfD4pPjmIoPaQ+lSl9ntH0tFLQdYfxdI68YWcgNNXZtNzrmP3S4pSC5t1w5NF5u3KZ/iqmJ8Caylj0yqFSCssdKtoFVQghhLhULMvieE+U5070cbo/ht1UrK8JsLkhxJNHezGV4v031evsGHRQ+FTf1zk6+jyfavl7ws567qj4aMnvpxSsrPCxqS6Ie3aTuOtA7Dc/pf8L/0D8yDmUzSRwy1rK7r1xMiCcERjmMnoFb6hNl7vkc3oERbRHB4DTmQ7dOK92qx43EV514WH2s1/fuHPqe6WgcYdeNbSV+PuTENcxCQ4XqD+laA1OrRzmDTsj/lVUu8cbxPhrIDkK6NXDBQWHs4YleZwmRGEwuIGcOTWzp6zU4BB0s5rWe8Azdx2EM1hJRtlQlg4RDbtDB7mL5LAZOK71OU5CCCGuCLm8xZsdwzx7oo+e0RQBl4371lcxHM9wsHOEN8ZLPD6wvZGQx0E6n2TvwA95efDHgMUt4d/Hb19YtozbYbBnTdWcswyvVemODkyfD1PFyB55kUz/CBUP3U7ojk1zp472HIbDP9ZzCf3VOngzbGB3Q8N28NdCoFZnLNncCwsEi6m7qXim1EX8XiPE9USCwwVIpHOMZQzCjql1tlHfSvKGnZBnPGDzV0PfMUAXp48k5t9nznTRUbUHC0Wgzg9VXt2eue84XkeGtD3IiK91xmvKPQu88xWonfdpj9NOvzOMO9kDgPIubtVwgt00cNolOBRCCHHppLN59p8d5LmT/QzHM1QFnLzvpnpuqA9OZuLcv7mG491RUtkcm+qCZPMZvnLmTxjO9LDOfzt3VX6coH1hM31tpuLO1ZXXTWBopdOMPfZDhn/8U2KvHqTio+8h8raNBHasIXDLGtT0rKd8Tq8Gxvr018AJ/TuRrxJ2/DFUrLm0B+urgsq1l/Y9hLjGSXC4AF3jkd5Ep1JL2Rjyrwb0bEFArxyOK6UwfTCwnqxN322ze4Lg9unVvuobcA2cpfetoYLc+gWtHJbA67SRmBYcGksRHNokOBRCCLF0huNp9p4eZCCWYiCaZiCWIpOzaCz38J4ballT7S+ocbMZButrA/SnzgFl2Aw728t/jyrnCuo9xRu6TVdf5qZzODHZP8BQcNuqyJJfh69ElmXR+5//MyM/+iG54VFsYT+R37+V4I11kM+ipmcIWXnoeBWO/VLXC05wBmHdA9Bym14tvJRMuy6lEUJcFAkOF+D8cBIAW7iJrooAaVuAvKkbr4QmgkO7Wwd3ieEL1t2l7QFGfSsmv58RUCmFI9KMPeggNW0khs1UU4HoEvE4TJLOqQJ95auYZ+sLc9oMnIsZaiuEEEIUkcnl+cZLbfSNpQh7nYR9DlZWeFlfG6QlMncnzJFML0/1fo0jY8/ywYa/ocm7mZvK7i/pPRvLPexeFWEknuHNzmHaBxNsbymnJuheqh/ripMbGSG+fz/+PXtQQPrwATyrqgndeR/eTc0zVwkn9ByGo4/BWBcEG2Dt/eCvAk8F2F2F218q9dsldVSIJSDB4QKcH185tIVXEHdP3TVUCgLTAzZ/LSSGcV0gtXIgtHnGqmCxVMy11X5ebZua0VO20JTSEngcNpKOcj3uwspj919ccOhzyT8rIYQQS+fxw930jKb46M5m1lT7L7h9Op/gpYEfsm/wxwDcGn6YGnfxlEa3wyCRntkfQCnYVKdHHgQ9dm5bVUEslS06g/iKl89DsaBunJXJEH3hBUZ+9jOiv3sSK5Oh9cnfYo+9Rf2f3Isy5ugMmk3CwR9C56u6id2NH4OazYuakXzRQo1Qsfryv68Q16Cr8FNu+XSNrxwGZgU/fpdND46ffKAaet/CaepZhXFHmL6ym6gcfBVnehCAhKuKuLt6xn6Krba1Vvg40RNlJKFTWcu9S1/jYBoKp9NByhHCno1hc1/4wjsf/9V48RRCCHFJZPN5Xj83TNdoktFEZvJ6tqUhxNaGsgt2+jzWPcZLpwbYuTJcUmBoWXm+efZ/oy/VxvrAndxZ8dE56wprQy62NIT49eEestMGCzeVewpqCq/KwHCoDUY6ofnWok/H9r5M51/8BbnBQcxQiNBDDxF8z/3Yhl+DWO/cgeFwOxz4hm4ys+ptsOq+i28ks1h2DzQV//mEEAt3FX7SLZ/bVkc4P5LANqsTZ8g9azXPXw1KYWBhOt10l99Czuaho2oPZaNHKBs9Rn9oc8H+i9XpGYZia2OIp4/1AZdm5RB0amnCWUHOcGC/yE6jsnIohBDCsiyOdI3yq0PdDMTSOG0GQbedoNtOPJ3jsTe7eOJwN5vqQuxeFaE6UJiCGE1l+dGBDqoDLt6+obrIu0w5nzhOtWslhjLZHf4gPlvZvHWFhoKtjWUE3XZubCpj3xl981Yp2Fh/DQxKz6bg3F49N9BXCZFVpE6fYfSxx3Bt3IB/zx4cK1rw3HwzwQfeg2/3bhRZOPFriA3N3FdqDEbPQ7QbRrug4xVw+mHn5yC8cnl+vgnNuy9v+qoQ1zj5LX4BbmwsYyCapm9s5kyeghpAmxPc5RAfIFl3K7nMeEtlpRgKrmfM30LWKKxZmKuJS23ITU3IRddwkrD30gyX9zhsDDorMMyLv/PnuxrvrgohhFgyA9EUP36tkzP9MSr9Tj62q5nVVTNX/TqHE+w7M8gb7cO8dm6ILQ0h7l5XRbnXQSaXp60/xtPH+0hmcnxid8ucNy6n1xXeX/MXbArezdrAhVeSVlX5Jq/frZU+ekeTtA3EaYl4Cbiukk6kYz3Qd0SPgGjYPjOls30fZBKke4cZ/fl/YfS1TlLHT4JShD/5CfwrHNhHOqn/swd1M730MJx5VgeC03W9oVcJrfHUW7tHzx/c8Ht6HuFyiqyCYN3yHoMQ1xj5LX4JhIq1s/bXQLAOZ7YBuqMznqoOl9MxNHPGhd1UBV3WpruxsYxfj3UTcF+avzKv0+S8M4LLfvG1Al6H/LMSQojrVTSV5WsvtpFI53jP5lpubi6fWXoxri7k5sGtdbxtfRXPnOjjpVMDvNkxQmPYQ8dQnEzOwmYo3rO5tuiqoq4r/AEvD/4YhcGt4Q+wxl9aeqHDZrCxbubq4M0t5QwnMgWPL7uRTug/BvFBvVrn9Ovmd8Pn9GMTMjFouQMMk2zbIWwDJwHo+Lufkjrbi3tVA1V/+S/x334z9thR6D+hXzd4Wn8V038cXvumrulb8y6dGeXwLU9d4Ww2J9RtW+6jEOKaI7/FL4FAse6hFWvA6cfXEy14alWVryA4vFBn06Dbzs3N5fMGkBfD47CRN+xkPfPPRCyFMVeNghBCiGtaOpvnmy+1MZbM8KndK2goLzKMfBaP08Y7Ntawa2WEp472cm4wzramclZX+WiJ+HDMkVXzg46/5lz8IBsCd3JnxccI2EtvpraxLlBQ5283De5ZVzXn+11yiSHoelPX7hk2QMFI+8yVvNmreuMsyyL15quMfetRxg6cId12htVf+ByG007Nx+/DFvJijwR1YDn0amnHM3wOXvkKeCvh5s8UHyy/VAxTN7VJjuhGN6Wo2ybppEJcAhIcXiTTKGxQA0y2U/bPes5mKKr8LrxOk1gqN/l4KXMBm+dp132xJlb7bKb8kxBCCLFwubzF915tp3MowR/uaCopMJwu6Lbz3q3zpwi2xw9T5VqBw3Bze+RDGMpGnXthQ899LhurK4s3trkkgWH7PvBWQHnL3NuMdcOpJ3Wd4AJFD56h++u/IdMzDArcq+qoeN9urLxOA3W3TrvpO0dwWXg8XfDyP+q00R2fvbSBIei6wfLx0V6ZhA4SrWkdZIfOQt/Rqe99ldKdVIhLRCKBixRw2eddzZsdHFYEnBiGwu+yLTg4vJQmusXZTVn1E0IIsTCWZfFffn2MI12jvPuGGtbXLu28ueF0D0/3fY0jY89xR+Qj7Ir8AQ2ejYva16pK3+XLcMmmoO+YngWYjkF1kWMePANtz0E+V/jcLLl4itjBM0RfO0Vw90a8G5uw+T04KssIv3M7/ptasYV8iz/e1Bic+A2cfUGnrt7yx+CaJ83WtEOgTv9sicG5fwanH2q2QDYBnfvBmuoMS+2WqcAQ9PvaZ/VlCNRCWbM+rnQUGncu8gcUQlyIBIcXaXar69l8ThuGgokO2RN1EwGXne6RqTuEyz003uvU7z+7E6sQQghxIcPxDM8e72d3a4SdKyNLtt9ULs7ewR/w8uBPUBjsDn+QbeUPLHp/SkFz+DI2Uek7Cvms/nPHKzr4qt0KqVG9Ohbr1/WE04OlWfKZLEO/PkD0jdPEj3VALo/hdeFZ1wCAq7mKxr98f5EX5uDcS3olbuWeuUdNWJbuQtp5AM48o4+3YYceUeEOFW6vFPiqdTOYUBNMZBzl8zpAjA9CchgSw5CJQ8VaiKyemrXoicDpp3X6aHmLPh+lCNTA+gd011RPeWmvEUIsmASHF6mgU+ksSim8ThtjSX1xqAnq4NA/qxOa077MK4d2PZPRLvWCQgghFqjM6+Bbn9rO3tODF954AX7V/T84MvYsGwJ3jdcVXlzgWR1wXXCu4pLJ56H36MzH+o7OTI8sIjuWIH74LPlMltBtG1E2k8HHX8X0ugi/82Z8W1bibq1FzXUz17Kg9y1462cQ69WP9RyCGz8CnvD4Nnm9otn9JvQe0cEc6NW9Ne8AX9XMfbrLoKxJP+6t0CuGsxkGeCP6az6BGlj3bv3eDTvm33Y2066PQwhxyUhweJFCJcwd9Lt0cOiyG5Pbz043Xe60UqUUboeJfZmPQwghxNUp5HFgLEHTtPb4IYL2KgL2CnZHPsDN5Q8suK5wLk3hS1w7N93gab1yVoL48U6iB04SO9xGsq0HLHDWR3RwqBQr/vaTmJ55RlnlczDaqd+z+yAMntJB3LZP6ZXANx+BZ/8f2PCgbnzT/rL+r80JkTVQ+TaoWFd8pdDhg9VvX9rmL04fNO1auv0JIZaMBIcXKXSBlUOYCgSrprXiLgwOlzetFHTHUpusHAohhFgGw+lunur7GkfHnuem0P3cV/3HRJyNS7Z/m6EW1iQnOQrDZ6F60+LesPdw0Yfz6QyJk+dJHO8k/J6dKEMx8twhhp89iHtlDZHfuxXf6gpcHNVpodWbMD1F6ghzWb1C2PmqXgXMjZeqeMJ6BmHTrVOppKEGOPBNeOO7+vvIalj3HqjaNJUWWowyYMWd0hVUiOuIBIcXwW7qlNELmUghnR4c+pw2TANy4824lnvlEMDrMOccMiyEEEJcCqlcnJcGvs++oZ9iYHBb5A/ZUf57F7XPkMfOaCIzWe8PUF/mLv0al0nCiV/rGsHE8HigtYDr42jXjBmEqc4BRp4/RPxYB8nT3VjZHCiFf8danDXlVPz+rVT94V0YTrtu2HL461Orjgd/AOFW3ZAln9MrgemYDgwzcb2yV38TlLfqxi7FVv88Ydj1z3XAGqibSi+9kIYd4Ct9RIgQ4uonweFFuFC94YSJVcKJekPQaZw+p52RRAa4MoJDj9OGTbqVCiGEuIye6/8Wrwz9jI2Bu7mz4iP4L7Ku0FBwa2uEgWhqRg1kyeOgclk4+dupsQ8DJ3UwtnIP2IqUkuTzuqFLLoOFQbq9i8Tzj5N48zChuzbjXllDuneYgV++grulmrK33YRnVRWecBJz6DkYMbE5fDrI63pd1weWtcDmhyGXga439OP9x6dmIJoOnQZaf5NOC52r2cyME2NC9Q2lnQPQgWbl0qTzCiGuHhIcXoRAycGhHZ/LVrDK6HfZpgWHV0JaqYkpaaVCCCEWY56Om7Odix/EaXiocq1kZ/j9rA/cQa17zZIcxqoqH0G3naDbTjyd482OEVx2Q9+gzab1iltZk26yMls+rztpxvpmPj7WBUceBX+NHu3gCmJZFlbvSYxEN5m+fs5/8ZckT3eRT6QBdEfRBhvus1/Bl82x5qM+DF8Csm9CTxt053XAZllTM/0Mu+7I2XKHTukECNbD2nfp7ZagpnNO7jLdcMbp11+B+WdOCiGuTRIcXoSAq7Tg0OswqQsV5utPrztc7m6loIPDfP7C2wkhhBCzqcQArlQ/SefcK39D6S6e6vsax8ZeYK3/Vh6s+9d4bWV4bUUCtQWwZaL44+dwmhabCEOHCe4QGyNVxFJebAao3iN6FS6b1P+t2axX0gxDB4XDbdDzVmFgOC7VdpbU2X0k23pJnu0hebaX4K71VH1oD6bPTT6ZJrBrPe6VNbhbKnGMvoQ68xi4qlFlzaj0mO4Kqky9ChlZo1cIDUOPm0hHwe7RgVnRE3wJA8PIaj07cCGps0KIa9KyBIdKqXLge0Az0AY8ZFnW0KxtGoBvAtVAHviSZVl/N/7c/wV8Gpj4BP/XlmX98nIc+3QBd2mnTylFa0Xhh/30lUfHFVDr53XYSGQuPIRXCCGEKGBZVA6+Snv1vVhqZjZMKhfnxYHv8crQTzEwuS3yoTnrCv2xszjTgzjTwzgyI2RsPhKuSuLOSlLOMHlj6tppZuOUj75FIHYWrDzNEQ+Ovu4Z+7vZ7iWTt6B/WudQKw/nX9MNZ4L10H8SMnEsyyI7GCXV2U+qox/DaaPsbj2H7+x//C65kRiYBs76CL4tKydnDRp2Gy1//WFdEzh8Fg5/G0bOQdNuWP8enQY6H4dXf10sZejOorkU9B2H0Y75V3SVgvqboWrDxb+3EOKasFwrh38F/M6yrM8rpf5q/Pu/nLVNFvgXlmUdUEr5gf1Kqd9YlvXW+PP/1bKs/3wZj7lAqTWHAEFP4bYTK4d2U2FcAemcbodJRpYOhRBCLJI9M0b5yFsMhGZ2+Hx9+FfsHfwhmwJ3c0fFR/HbizdEqRjcTyB6esZjzvQQzvQQIY4BkDfsZE0POdOFK9WPsvRNTbfDpNJfmKVjZGLMHgKRT6ZJ9wyTG2vDu1HXJZ7/0q8Ye+XYZFoogHt13WRwWPtH78QW9OCsi6BsJuTSehxE31E9zH7ghO4amk2C3Q03fVyvTl5OtVvBPz6jMNQIqSiMtOvmOIkhSI7oQNXuBodHrxgG6y/vMQohrmjLFRw+ANw5/udvAE8zKzi0LKsL6Br/85hS6ghQB7zFFcA0dMfRizGRluq0L3+9IYDLbpLJSXAohBBXMqXU24G/A0zgy5ZlfX7W82r8+XcCceBjlmUduFzHFxo7TtRTz/FMOzmyrPDeyI1l99PovYEa16riLxpfdfTH2i64fyOfwZEfgczI5GNKQXPYw8RtViubIzM4RnZwDM9avbo3+JvXGH3pCJm+YbJDUb0vt4PVX/ozlFI4a8sxdm/AURvBWR/GWR/B5vfolbdYL77ged2FtKsXor1Tg+MnuIJ6iHzFWqhYrVNEL4Zh051JSxWohZpZDWecPqhcd3HHIYS4rixXcFg1HvxhWVaXUqpyvo2VUs3AVuDlaQ9/Tin1EeBV9Arj0Byv/QzwGYDGxqWbl+Rz2lEXmf/vspvYTXVFdCqdcLEBrxBCiEtHKWUCfw/cC3QAryilHp2WVQPwDmDV+NcO4B/G/3tZ9OaG+Un7v+ONzCka3ZtY4b0Ru+FcksCQTBYVS6JiCYxYEjUWJ7NpBXVVAfKvHOPMrw+QGRwjNxqD8WzK1V/6M0yPEyuVRhkK78ZmHNVlOKrKcFRP1TqG375Fj3pIjkJ+AM4fhsSg7hKaHA9EbS7wVurREt4K8JSDu1z/1xVaeF2gMqaa0Uxn2mHt/Tr1daiE82J3Q/NtC3tvIYQo4pJFAkqp36LrBWf7Pxa4Hx/wI+DPLcsaHX/4H4D/gP7o/w/AfwE+Uez1lmV9CfgSwLZt20pvpXYBpdYbXng/9isqOLzYgFcIIcQltR04aVnWaQCl1CPobJzpweEDwDcty7KAvUqpkFKqZuKm7KUylo7yo8Q+nsocwWYZPOjczk5jNwy8SVllFT1DGRgZgxyoTBp3tBdXrA+z0onpymP0DmF7qw2VyqCSKVQijUqmSN5/K/lIEPsrR3E/+nzB+/rX1lEXcjNiGpg+N66mSmxlPuyRAPZwEDWenRO+fwfhd22HwdN6Rc4ZAJcPxs7D2Zf0MPlscubOHT4dCEZW6y9P+OIbwygF/lqIrNIdQU89qbuhTtdwi55X2HKHrmMcaZ97f3YPNO/WaaJCCHGRLllwaFnWPXM9p5TqmbhQKaVqgN45trOjA8NvW5b142n77pm2zT8Bjy3dkZdmIfWG8/G7bCgkIBNCCFGSOmB6pNBB4apgsW3qGC/VuFQO7X2Uj//Nm3x88pEXgReJ/8Ee1r19K52Hj5P94s8LXhf/8NvIrm7A6B/B9bv9WDYTy+XQX24nZHRqpdVQgfnOHcQdDnIeF5bPjavcx6q1NQAEd60nuGv93AeYGIKDP9Srg7MZNp0S2rgT/NV65c6wTY2TKJXDqxu8OLx6JEY6NvP5UIMO/Jy+qcda74bjT0x1SS1fAZHW8eMyYOVdcOI3UwGkYerVymAdBBvBW+JAeyGEKMFy5RA+CnwU+Pz4f382e4PxmomvAEcsy/p/Zz03/Q7og8ChS3u4hUodY1HKflJZqfMTQghRkmJ3E2dnxZSyzZKXXdy87j6O7tlHwHDr1TEFmAa+lio8dpPK1bWcfN+dYJpgN7HsNiyHDVt1OQDZNY2M/ruPwxxzfxs2NVK5cxU5y2IwlmYglqYl7MVWLPtmYnZgPqu/zr8GR3+uR1ase2C8WcsIJMd0sFW7dWHdQg0beCN6pTGThHwGKjfoBjTm+K9W694Np5/RQZ1ph/rtuhZxNtMOq+6F44/rofeNO2e9l6kDyKGzU+mrMnJCCHGJLFdw+Hng+0qpTwLngPcDKKVq0cX17wRuBT4MHFRKvT7+uomRFf+3UmoL+mLXBvzRZT16Zo6huBh+lw2SCyg4F0IIcT3rABqmfV8PnF/ENktedmGriOC6ayfpWY+31gYACFYGce1YS3TaNU8pWFUTYDCWpmtkZkqnymcJRU+SdJQTrGqk0qd7jppKUeFzUjH+PWPdcO4lGDgF2YSeGZhNFtbyRVbDpod0ULfoH9KpG85Urgd7YWfUGexuWP026DkEoSZwBebf76r7IB0HW5GxF6Z9ajVRCCEuoWUJDi3LGgDuLvL4eXR3NSzLep7idz+xLOvDl/QASxBwLVHNocsuHUKFEEKU6hVglVKqBegEHgY+OGubR9FN2x5Bp5yOXOp6QwDHiV/gSXQRd1VNpmP6XTb80xqdVQdcnExGJ7+v9DvxOW14nTZiqSyjySxmLkHV4H6qBvfhyOptraGVuuFK1UZIj0F8QHcM7XxV1xAqA8Kr9BgHm1sHbsZ4aqhhgieiZ/ldTL1guBUab9GBWqmUgupNF94OdDBpdy/u2IQQYolIa8pF8DhMbEs0tN7vshFNycqhEEKIC7MsK6uU+hzwBHqUxVctyzqslPrs+PNfBH6JvtF6Ej3K4uNz7W/JZJL4nvw/2JTPkDVdjHqaiHoaKI9UwmBEj3lwBinzOHDYDNLZPP7cIA2DbXDsCCoTZ41lkcrmcaSHMPMZhr0r6Gl+kFrbMObZF+DA1wvf1xOBte+Ghu3g9F/cz2DawV+jm7/MHhzvDul0T1N+bRJCXNvkU24RlqoZDYDNNAh5lm5/Qgghrm3j5RW/nPXYF6f92QL+9LIelN3F8B8+wcCT/4NArI1ArI3ysWPQM3Mzw+Flgz1ALpPBne7XDwbrwVuBAdhyeQZz9aTqdlFe00KDY7z+cOVd0PMWDJ/VgZonDO6wbsay0KYxs/mqdOfQsmYdIPYdg7MvTjtoU3cNlcBQCHEdkE+6RViqMRYT/EvU3EYIIYRYLnlfFQOhTQyEdBrlypAiYib1sPjkyPh/R7Elhsmks7Dqdp1y6Z6aNWgHwhYYs7M/lQHVG/XXUlBKB4NVmwq7fVas0Y1hOl7R39dt041ghBDiOiDB4SIsVadSIYQQ4loUcNuIlI03YAnUzHjOANwWc3QVKBIYLrXyFVB34/xpqNUbdQfS2ABUzTMeQwghrjESHC7CUnUqFUIIIa41pqFYEfHNu80lDwCLMe16xmCpXT9rt0JOegIIIa4vEhwugqwcCiGEEMU1hj04i80eXE6eMKy4QzfGWQipMxRCXGfkU2+BHDYDt6P4gF4hhBDielbmdUzOIyyZ3QOZ+NIfjLtMN7sJ1OmmMzI4XgghLkiCwwXyL9F8QyGEEOJa4rQZtIQ9+hvTrtMyrTxk05BLQToGqVFIRXWDmbJm3SXUXw3nX4Pzrxfu1BWEdBTyubnf2O6BQC34KvWfHV79ZVtgkCqEEEKCw4WSlFIhhBCikM9pB9PQg+db79UD6YuxLB00GtOycGq36oCx88DUY1UbdKfQTEx3Dh06O/WcKwjhlRBskE6iQgixhCQ4XKClHmMhhBBCXDMMG7TeM3dgCHqMhCpSnlGzWT/efRCad0OoQT/u9MPKPTDaBcPnoLxFrxIKIYRYchLpLFBQOpUKIYQQhQxTB3GzRlcsSPVGqFhbvBFMoObi9i2EEOKCJDhcIKdNmtEIIYQQBZYqvVM6hAohxLKR1l1CCCGEEEIIISQ4FEIIIYQQQgghwaEQQgghhBBCCCQ4FEIIIYQQQgiBBIdCCCGEEEIIIZDgUAghhBBCCCEEEhwKIYQQQgghhECCQyGEEEIIIYQQSHAohBBCCCGEEAIJDoUQQgghhBBCIMGhEEIIIYQQQggkOBRCCCGEEEIIgQSHQgghhBBCCCGQ4FAIIYQQQgghBKAsy1ruY7hslFJ9wNmL3E0E6F+Cw7mWyDkpTs5LITknheScFFqKc9JkWVbFUhzM9WCJro8g/56LkXNSSM5JITknheScFHdJr5HXVXC4FJRSr1qWtW25j+NKIuekODkvheScFJJzUkjOydVL/u4KyTkpJOekkJyTQnJOirvU50XSSoUQQgghhBBCSHAohBBCCCGEEEKCw8X40nIfwBVIzklxcl4KyTkpJOekkJyTq5f83RWSc1JIzkkhOSeF5JwUd0nPi9QcCiGEEEIIIYSQlUMhhBBCCCGEEBIczkkp9Xal1DGl1Eml1F8VeV4ppf77+PNvKqVuXI7jvJxKOCd/OH4u3lRKvaiU2rwcx3k5XeicTNvuZqVUTin1vst5fMuhlHOilLpTKfW6UuqwUuqZy32My6GE/3+CSqmfK6XeGD8vH1+O47xclFJfVUr1KqUOzfH8dfcZe7WQ62Nxco0sJNfIQnKNLCTXx0LLeo20LEu+Zn0BJnAKWAE4gDeA9bO2eSfwK0ABtwAvL/dxXwHnZBdQNv7nd8g5mbHdk8Avgfct93Ev9zkBQsBbQOP495XLfdxXyHn518Dfjv+5AhgEHMt97JfwnNwO3AgcmuP56+oz9mr5kuvjRZ0XuUbKNVKukYs7J9fV9XH851y2a6SsHBa3HThpWdZpy7LSwCPAA7O2eQD4pqXtBUJKqZrLfaCX0QXPiWVZL1qWNTT+7V6g/jIf4+VWyr8TgH8G/AjovZwHt0xKOScfBH5sWdY5AMuy5LxoFuBXSinAh774ZS/vYV4+lmU9i/4Z53K9fcZeLeT6WJxcIwvJNbKQXCMLyfWxiOW8RkpwWFwd0D7t+47xxxa6zbVkoT/vJ9F3NK5lFzwnSqk64EHgi5fxuJZTKf9OVgNlSqmnlVL7lVIfuWxHt3xKOS//E1gHnAcOAn9mWVb+8hzeFel6+4y9Wsj1sTi5RhaSa2QhuUYWkuvj4lyyz1nbUuzkGqSKPDa7rWsp21xLSv55lVJ3oS98uy/pES2/Us7JfwP+0rKsnL7hdc0r5ZzYgJuAuwE38JJSaq9lWccv9cEto1LOy9uA14E9wErgN0qp5yzLGr3Ex3alut4+Y68Wcn0sTq6RheQaWUiukYXk+rg4l+xzVoLD4jqAhmnf16PvVix0m2tJST+vUuoG4MvAOyzLGrhMx7ZcSjkn24BHxi96EeCdSqmsZVk/vSxHePmV+v9Ov2VZMSCmlHoW2Axcqxc+KO28fBz4vKWLCU4qpc4Aa4F9l+cQrzjX22fs1UKuj8XJNbKQXCMLyTWykFwfF+eSfc5KWmlxrwCrlFItSikH8DDw6KxtHgU+Mt4t6BZgxLKsrst9oJfRBc+JUqoR+DHw4Wv4Dtd0FzwnlmW1WJbVbFlWM/BD4E+u4YselPb/zs+A25RSNqWUB9gBHLnMx3m5lXJezqHvFKOUqgLWAKcv61FeWa63z9irhVwfi5NrZCG5RhaSa2QhuT4uziX7nJWVwyIsy8oqpT4HPIHuovRVy7IOK6U+O/78F9Fdtd4JnATi6Lsa16wSz8m/BcLAF8bvAmYty9q2XMd8qZV4Tq4rpZwTy7KOKKUeB94E8sCXLcsq2qr5WlHiv5X/AHxdKXUQnS7yl5Zl9S/bQV9iSqnvAncCEaVUB/DvADtcn5+xVwu5PhYn18hCco0sJNfIQnJ9LG45r5FKr9AKIYQQQgghhLieSVqpEEIIIYQQQggJDoUQQgghhBBCSHAohBBCCCGEEAIJDoUQQgghhBBCIMGhEEIIIYQQQggkOBRCCCGEEEIIgQSHQgghhBBCCCGQ4FCIa5ZS6mal1JtKKZdSyquUOqyU2rjcxyWEEEIsJ7k+CjE3ZVnWch+DEOISUUr9R8AFuIEOy7L+/8t8SEIIIcSyk+ujEMVJcCjENUwp5QBeAZLALsuycst8SEIIIcSyk+ujEMVJWqkQ17ZywAf40XdIhRBCCCHXRyGKkpVDIa5hSqlHgUeAFqDGsqzPLfMhCSGEEMtOro9CFGdb7gMQQlwaSqmPAFnLsr6jlDKBF5VSeyzLenK5j00IIYRYLnJ9FGJusnIohBBCCCGEEEJqDoUQQgghhBBCSHAohBBCCCGEEAIJDoUQQgghhBBCIMGhEEIIIYQQQggkOBRCCCGEEEIIgQSHQgghhBBCCCGQ4FAIIYQQQgghBBIcCiGEEEIIIYQA/j+DTbxtab7dHQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"LinearDML with 2d-Poly Features\")\n",
    "plt.plot(X_test[:, 0], te_pred[:, 0], label='DML estimate1')\n",
    "plt.fill_between(X_test[:, 0], lb[:, 0], ub[:, 0], alpha=.4)\n",
    "plt.plot(X_test[:, 0], te_pred[:, 1], label='DML estimate2')\n",
    "plt.fill_between(X_test[:, 0], lb[:, 1], ub[:, 1], alpha=.4)\n",
    "expected_te1 = np.array([x_i[0] for x_i in X_test])\n",
    "expected_te2=np.array([x_i[0]**2 for x_i in X_test]).flatten()\n",
    "plt.plot(X_test[:, 0], expected_te1, '--', label='True effect1')\n",
    "plt.plot(X_test[:, 0], expected_te2, '--', label='True effect2')\n",
    "plt.ylabel(\"Treatment Effect\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.legend()\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"CausalForestDML\")\n",
    "plt.plot(X_test[:, 0], te_pred2[:, 0], label='DML estimate1')\n",
    "plt.fill_between(X_test[:, 0], lb2[:, 0], ub2[:, 0], alpha=.4)\n",
    "plt.plot(X_test[:, 0], te_pred2[:, 1], label='DML estimate2')\n",
    "plt.fill_between(X_test[:, 0], lb2[:, 1], ub2[:, 1], alpha=.4)\n",
    "expected_te1 = np.array([x_i[0] for x_i in X_test])\n",
    "expected_te2=np.array([x_i[0]**2 for x_i in X_test]).flatten()\n",
    "plt.plot(X_test[:, 0], expected_te1, '--', label='True effect1')\n",
    "plt.plot(X_test[:, 0], expected_te2, '--', label='True effect2')\n",
    "plt.ylabel(\"Treatment Effect\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Tree Interpreter\n",
    "\n",
    "Interpreting heterogeneity via a tree based rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<econml.cate_interpreter._interpreters.SingleTreeCateInterpreter at 0x13755a2a400>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from econml.cate_interpreter import SingleTreeCateInterpreter\n",
    "\n",
    "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2)\n",
    "intrp.interpret(est, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "intrp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Example Usage with Single Continuous Treatment Observational Data\n",
    "\n",
    "We applied our technique to Dominick’s dataset, a popular historical dataset of store-level orange juice prices and sales provided by University of Chicago Booth School of Business. \n",
    "\n",
    "The dataset is comprised of a large number of covariates $W$, but researchers might only be interested in learning the elasticity of demand as a function of a few variables $x$ such\n",
    "as income or education. \n",
    "\n",
    "We applied the `LinearDML` to estimate orange juice price elasticity\n",
    "as a function of income, and our results, unveil the natural phenomenon that lower income consumers are more price-sensitive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A few more imports\n",
    "import os\n",
    "import pandas as pd\n",
    "import urllib.request\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the data\n",
    "file_name = \"oj_large.csv\"\n",
    "\n",
    "if not os.path.isfile(file_name):\n",
    "    print(\"Downloading file (this might take a few seconds)...\")\n",
    "    urllib.request.urlretrieve(\"https://econmldata.azurewebsites.net/datasets/OrangeJuice/oj_large.csv\",\n",
    "                               file_name)\n",
    "oj_data = pd.read_csv(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>brand</th>\n",
       "      <th>week</th>\n",
       "      <th>logmove</th>\n",
       "      <th>feat</th>\n",
       "      <th>price</th>\n",
       "      <th>AGE60</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>ETHNIC</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>HHLARGE</th>\n",
       "      <th>WORKWOM</th>\n",
       "      <th>HVAL150</th>\n",
       "      <th>SSTRDIST</th>\n",
       "      <th>SSTRVOL</th>\n",
       "      <th>CPDIST5</th>\n",
       "      <th>CPWVOL5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>tropicana</td>\n",
       "      <td>40</td>\n",
       "      <td>9.018695</td>\n",
       "      <td>0</td>\n",
       "      <td>3.87</td>\n",
       "      <td>0.232865</td>\n",
       "      <td>0.248935</td>\n",
       "      <td>0.11428</td>\n",
       "      <td>10.553205</td>\n",
       "      <td>0.103953</td>\n",
       "      <td>0.303585</td>\n",
       "      <td>0.463887</td>\n",
       "      <td>2.110122</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>1.92728</td>\n",
       "      <td>0.376927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>tropicana</td>\n",
       "      <td>46</td>\n",
       "      <td>8.723231</td>\n",
       "      <td>0</td>\n",
       "      <td>3.87</td>\n",
       "      <td>0.232865</td>\n",
       "      <td>0.248935</td>\n",
       "      <td>0.11428</td>\n",
       "      <td>10.553205</td>\n",
       "      <td>0.103953</td>\n",
       "      <td>0.303585</td>\n",
       "      <td>0.463887</td>\n",
       "      <td>2.110122</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>1.92728</td>\n",
       "      <td>0.376927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>tropicana</td>\n",
       "      <td>47</td>\n",
       "      <td>8.253228</td>\n",
       "      <td>0</td>\n",
       "      <td>3.87</td>\n",
       "      <td>0.232865</td>\n",
       "      <td>0.248935</td>\n",
       "      <td>0.11428</td>\n",
       "      <td>10.553205</td>\n",
       "      <td>0.103953</td>\n",
       "      <td>0.303585</td>\n",
       "      <td>0.463887</td>\n",
       "      <td>2.110122</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>1.92728</td>\n",
       "      <td>0.376927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>tropicana</td>\n",
       "      <td>48</td>\n",
       "      <td>8.987197</td>\n",
       "      <td>0</td>\n",
       "      <td>3.87</td>\n",
       "      <td>0.232865</td>\n",
       "      <td>0.248935</td>\n",
       "      <td>0.11428</td>\n",
       "      <td>10.553205</td>\n",
       "      <td>0.103953</td>\n",
       "      <td>0.303585</td>\n",
       "      <td>0.463887</td>\n",
       "      <td>2.110122</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>1.92728</td>\n",
       "      <td>0.376927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>tropicana</td>\n",
       "      <td>50</td>\n",
       "      <td>9.093357</td>\n",
       "      <td>0</td>\n",
       "      <td>3.87</td>\n",
       "      <td>0.232865</td>\n",
       "      <td>0.248935</td>\n",
       "      <td>0.11428</td>\n",
       "      <td>10.553205</td>\n",
       "      <td>0.103953</td>\n",
       "      <td>0.303585</td>\n",
       "      <td>0.463887</td>\n",
       "      <td>2.110122</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>1.92728</td>\n",
       "      <td>0.376927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store      brand  week   logmove  feat  price     AGE60      EDUC   ETHNIC  \\\n",
       "0      2  tropicana    40  9.018695     0   3.87  0.232865  0.248935  0.11428   \n",
       "1      2  tropicana    46  8.723231     0   3.87  0.232865  0.248935  0.11428   \n",
       "2      2  tropicana    47  8.253228     0   3.87  0.232865  0.248935  0.11428   \n",
       "3      2  tropicana    48  8.987197     0   3.87  0.232865  0.248935  0.11428   \n",
       "4      2  tropicana    50  9.093357     0   3.87  0.232865  0.248935  0.11428   \n",
       "\n",
       "      INCOME   HHLARGE   WORKWOM   HVAL150  SSTRDIST   SSTRVOL  CPDIST5  \\\n",
       "0  10.553205  0.103953  0.303585  0.463887  2.110122  1.142857  1.92728   \n",
       "1  10.553205  0.103953  0.303585  0.463887  2.110122  1.142857  1.92728   \n",
       "2  10.553205  0.103953  0.303585  0.463887  2.110122  1.142857  1.92728   \n",
       "3  10.553205  0.103953  0.303585  0.463887  2.110122  1.142857  1.92728   \n",
       "4  10.553205  0.103953  0.303585  0.463887  2.110122  1.142857  1.92728   \n",
       "\n",
       "    CPWVOL5  \n",
       "0  0.376927  \n",
       "1  0.376927  \n",
       "2  0.376927  \n",
       "3  0.376927  \n",
       "4  0.376927  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oj_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data\n",
    "Y = oj_data['logmove'].values\n",
    "T = np.log(oj_data[\"price\"]).values\n",
    "scaler = StandardScaler()\n",
    "W1 = scaler.fit_transform(oj_data[[c for c in oj_data.columns\n",
    "                                   if c not in ['price', 'logmove', 'brand', 'week', 'store','INCOME']]].values)\n",
    "W2 = pd.get_dummies(oj_data[['brand']]).values\n",
    "W = np.concatenate([W1, W2], axis=1)\n",
    "X=scaler.fit_transform(oj_data[['INCOME']].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Generate test data\n",
    "min_income = -1\n",
    "max_income = 1\n",
    "delta = (1 - (-1)) / 100\n",
    "X_test = np.arange(min_income, max_income + delta - 0.001, delta).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Train Estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "est = LinearDML(model_y=RandomForestRegressor(),model_t=RandomForestRegressor())\n",
    "est.fit(Y, T, X=X, W=W)\n",
    "te_pred=est.effect(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Performance Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Oranje Juice elasticity as a function of income\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(X_test, te_pred, label=\"OJ Elasticity\")\n",
    "plt.xlabel(r'Scale(Income)')\n",
    "plt.ylabel('Orange Juice Elasticity')\n",
    "plt.legend()\n",
    "plt.title(\"Orange Juice Elasticity vs Income\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4. Confidence Intervals\n",
    "\n",
    "We can also get confidence intervals around our predictions by passing an additional `inference` argument to `fit`.  All estimators support bootstrap intervals, which involves refitting the same estimator repeatedly on subsamples of the original data, but `LinearDML` also supports a more efficient approach which can be achieved by leaving inference set to the default of `'auto'` or by explicitly passing `inference='statsmodels'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "est.fit(Y, T, X=X, W=W)\n",
    "te_pred=est.effect(X_test)\n",
    "te_pred_interval = est.const_marginal_effect_interval(X_test, alpha=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Oranje Juice elasticity as a function of income\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(X_test.flatten(), te_pred, label=\"OJ Elasticity\")\n",
    "plt.fill_between(X_test.flatten(), te_pred_interval[0], te_pred_interval[1], alpha=.5, label=\"1-99% CI\")\n",
    "plt.xlabel(r'Scale(Income)')\n",
    "plt.ylabel('Orange Juice Elasticity')\n",
    "plt.title(\"Orange Juice Elasticity vs Income\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Example Usage with Multiple Continuous Treatment, Multiple Outcome Observational Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the same data, but in this case, we want to fit the demand of multiple brand as a function of the price of each one of them, i.e. fit the matrix of cross price elasticities. It can be done, by simply setting as $Y$ to be the vector of demands and $T$ to be the vector of prices. Then we can obtain the matrix of cross price elasticities.\n",
    "\n",
    "\\begin{align}\n",
    "Y=[Logmove_{tropicana},Logmove_{minute.maid},Logmove_{dominicks}] \\\\\n",
    "T=[Logprice_{tropicana},Logprice_{minute.maid},Logprice_{dominicks}] \\\\\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1. Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the data\n",
    "oj_data = pd.read_csv(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data\n",
    "oj_data['price'] = np.log(oj_data[\"price\"])\n",
    "# Transform dataset.\n",
    "# For each store in each week, get a vector of logmove and a vector of logprice for each brand.\n",
    "# Other features are store specific, will be the same for all brands.\n",
    "groupbylist = [\"store\", \"week\", \"AGE60\", \"EDUC\", \"ETHNIC\", \"INCOME\",\n",
    "               \"HHLARGE\", \"WORKWOM\", \"HVAL150\",\n",
    "               \"SSTRDIST\", \"SSTRVOL\", \"CPDIST5\", \"CPWVOL5\"]\n",
    "oj_data1 = pd.pivot_table(oj_data,index=groupbylist,\n",
    "                          columns=oj_data.groupby(groupbylist).cumcount(),\n",
    "                          values=['logmove', 'price'],\n",
    "                          aggfunc='sum').reset_index()\n",
    "oj_data1.columns = oj_data1.columns.map('{0[0]}{0[1]}'.format)\n",
    "oj_data1 = oj_data1.rename(index=str,\n",
    "                           columns={\"logmove0\": \"logmove_T\",\n",
    "                                    \"logmove1\": \"logmove_M\",\n",
    "                                    \"logmove2\":\"logmove_D\",\n",
    "                                    \"price0\":\"price_T\",\n",
    "                                    \"price1\":\"price_M\",\n",
    "                                    \"price2\":\"price_D\"})\n",
    "\n",
    "# Define Y,T,X,W\n",
    "Y = oj_data1[['logmove_T', \"logmove_M\", \"logmove_D\"]].values\n",
    "T = oj_data1[['price_T', \"price_M\", \"price_D\"]].values\n",
    "scaler = StandardScaler()\n",
    "W=scaler.fit_transform(oj_data1[[c for c in groupbylist if c not in ['week', 'store', 'INCOME']]].values)\n",
    "X=scaler.fit_transform(oj_data1[['INCOME']].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Generate test data\n",
    "min_income = -1\n",
    "max_income = 1\n",
    "delta = (1 - (-1)) / 100\n",
    "X_test = np.arange(min_income, max_income + delta - 0.001, delta).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2. Train Estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "est = LinearDML(model_y=MultiTaskElasticNetCV(cv=3, tol=1, selection='random'),\n",
    "                model_t=MultiTaskElasticNetCV(cv=3),\n",
    "                featurizer=PolynomialFeatures(1))\n",
    "est.fit(Y, T, X=X, W=W)\n",
    "te_pred = est.const_marginal_effect(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3. Performance Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Oranje Juice elasticity as a function of income\n",
    "plt.figure(figsize=(18, 10))\n",
    "dic={0:\"Tropicana\", 1:\"Minute.maid\", 2:\"Dominicks\"}\n",
    "for i in range(3):\n",
    "    for j in range(3):\n",
    "        plt.subplot(3, 3, 3 * i + j + 1)\n",
    "        plt.plot(X_test, te_pred[:, i, j],\n",
    "                 color=\"C{}\".format(str(3 * i + j)),\n",
    "                 label=\"OJ Elasticity {} to {}\".format(dic[j], dic[i]))\n",
    "        plt.xlabel(r'Scale(Income)')\n",
    "        plt.ylabel('Orange Juice Elasticity')\n",
    "        plt.legend()\n",
    "plt.suptitle(\"Orange Juice Elasticity vs Income\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Findings**: Look at the diagonal of the matrix, the TE of OJ prices are always negative to the sales across all the brand, but people with higher income are less price-sensitive. By contrast, for the non-diagonal of the matrix, the TE of prices for other brands are always positive to the sales for that brand, the TE is affected by income in different ways for different competitors. In addition, compare to previous plot, the negative TE of OJ prices for each brand are all larger than the TE considering all brand together, which means we would have underestimated the effect of price changes on demand. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4. Confidence Intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "te_pred_interval = est.const_marginal_effect_interval(X_test, alpha=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SSuWcBiiUUkoppZRSSimVcxqgUEoppZRSSimlVM5pgEIppZRSSimllFI5pwEKpZRSSimllFJK5ZwGKJRSSimllFJKKZVzGqBQSimllFJKKaVUzmmAQimllFJKKaWUUjmnAQqllFJKKaWUUkrlnAYolFJKKaWUUkoplXMaoFBKKaWUUkoppVTOaYBCKaWUUkoppZRSOacBCqWUUkoppZRSSuWcBiiUUkoppZRSSimVcxqgUEoppZRSSimlVM5pgEIppZRSSimllFI5pwEKpZRSSimllFJK5ZwGKJRSSimllFJKKZVzGqBQSimllFJKKaVUzmmAQimllFJKKaWUUjmnAQqllFJKKaWUUkrlnAYolFJKKaWUUkoplXMaoFBKKaWUUkoppVTOaYBCKaWUUkoppZRSOacBCqWUUkoppZRSSuWcBiiUUkoppZRSSimVcxqgUEoppZRSSimlVM45ct2BiVBWVmbmzp2b624opdQAL730UosxpjzX/TgcdBxWSk1G02kcBh2LlVKT03BjcV4GKObOncuLL76Y624opdQAIrIz1304XHQcVkpNRtNpHAYdi5VSk9NwY7FO8VBKKaWUUkoppVTOaYBCKaWUUkoppZRSOacBCqWUUkoppZRSSuVcXtagyCYWi7Fnzx56e3tz3RWlphyPx8PMmTNxOp257opSU4q+9yiVG/q+pUZDx2ylxtdoxuJpE6DYs2cPwWCQuXPnIiK57o5SU4YxhtbWVvbs2UNtbW2uu6PUlKLvPUodfvq+pUZLx2ylxs9ox+JpM8Wjt7eX0tJSHWyUOkQiQmlpqX6bkJJIGuoaQzR0Tq3nQ0TOEpFNIrJFRD4/TLs1IpIQkfdl3LZDRNaLyKsiMnHl4EONsO9V6OuesEMcbvreo9Thp+9bo2eMYX3zelp6WnLdlZzQMVup8TPasXjaZFAAOtgoNUr6fwd6+hK8truDRzc2sru9hwuPnkFVYVWuuzUiImIHvg+cAewBXhCRB4wxb2Zp9zXgr1l2c6oxZmI/sTZvhOdvB08hVBwBs46D8sXW9SlM//8odfjp/7vR+82m32AwzC2cy/HVx7OweCEuuyvX3Tps9LWj1PgZzf+naRWgUEqpQ9UcivLc9lae2tJCLG4o8jkp8Ey5ofNYYIsxZhuAiPwaOA94c1C7TwD3AmsOb/cyuINQMAPad0LTm4BAyTyYtdYKWvhLc9Y1pZSaDpIkqfHX0BRp4lcbf4XT7mRN5RpWVayiyl+lJ/BKqQk1baZ4TAZ79uzhvPPOY+HChcyfP5//9//+H319fQA8/vjjvOc97zlgm8cff5zCwkJWrlyZvjzyyCMABAKBUfXjO9/5DpFIJH393e9+Nx0dHUO2v+222/j5z38OwF133cW+fftGfKzzzz+flStXsmDBggGP4+mnnx5V3wH27dvH+973voM3HGcdHR384Ac/GHH71tbW9OOtqqpixowZ6ev9f/fReOCBB7j55ptHvb06uGTSsKUpzJ1PbuNrD23kn3XNFPtczCj24ndPueAEwAxgd8b1Panb0kRkBnA+cFuW7Q3wNxF5SUSuHOogInKliLwoIi82NzePvrdiA18pFM62ghXhZnjtbvjbF+Gxr8CWR6GrHowZ/TGmken43gPw9re/ndmzZ2MyXifvfe970/0f63vJ/fffz5tvDo7xTazM5yTTjh07WLp0adbbf/nLX454/+vXr0//vUtKSqitrWXlypWcfvrpE9JvNXmJCEXuImoCNRS5ini24Vm+/+r3+d6r3+OlxpeIxCIH34kalek8Zi9evJjly5ezZMkSrr766mGPdyhG8tl5JO8Jc+fOpaXl0JNJD/X5+Pd//3dWrlzJkUceidfrTf9Nf/e73x3ysTO97W1vG9P2h8uU/KQ9FRljuOCCC/jYxz7GH/7wBxKJBFdeeSXXXXcdX//614fd9qSTTuJPf/rTuPXlO9/5Dh/+8Ifx+XwAPPjgg8O2v+qqq9K/33XXXSxdupSampoRHeu+++4DrIHzG9/4xgGPIx6P43Ac2suwpqZmzP9BR6M/QPHxj398RO1LS0t59dVXAbj++usJBAJ85jOfGdBmNI//3HPP5dxzzz2kbdTI9MYSrN/TyaMbG2kORfE47VQXerBN/W+Lsj2AwWf33wGuMcYksnw7doIxZp+IVAAPi8hGY8wTB+zQmNuB2wFWr149PtEDsYG3yLoYA30h2JD6/+8tgdlroXIpFM0Gm31cDplPput7T7+ioiKeeuopTjzxRDo6Oqivr0/fN9b3kvvvv5/3vOc9HHnkkaPex6HKfE5Goj9A8cEPfnBE7ZctW5Z+31q3bh3vec97DvjAPpr3rUPtt5pcnHYnVb4qjDF0x7q5b/N9/EH+wPLy5RxTeQxzCuZgE/3OczxM9zH77rvvZvXq1fT19XHttddy3nnn8Y9//OPQOz/ISD47T+T5xaE+H9///vcBawx/z3vekx6X+yUSCez2Q//MM5YviA+nnIwmIvJ1EdkoIq+LyH0iUpSlzeJUQbb+S5eIfOrw93Z8PPbYY3g8Hi677DIA7HY73/72t7nzzjsHRCdHIxwOc9ppp3H00UezbNky/vCHPwDQ3d3N2WefzYoVK1i6dCn33HMP3/3ud9m3bx+nnnoqp556KjAwGvjzn/+c5cuXs2LFCi655BLAOrn+xje+we9+9ztefPFFPvShD7Fy5Ur+/Oc/c/7556f78fDDD3PBBRcctL933XUXF110Eeeccw5nnnkmbW1tvPe972X58uWsXbuW119/PX3cSy65hHe84x0sXLiQO+64Axj4TVEikeAzn/kMy5YtY/ny5fzv//4vADfeeCNr1qxh6dKlXHnllelv0N7+9rdzzTXXcOyxx7Jo0SL++c9/pvd50kkncfTRR3P00Udn/Q/8+c9/nq1bt7Jy5Uo++9nPYozhs5/9LEuXLmXZsmXcc889I/p7rVu3jk9/+tOceuqpXHPNNbz66qusXbuW5cuXc/7559Pe3p7u66c+9Sne9ra3sXTpUp5//vn083f11VcD0NjYyPnnn8+KFStYsWJFut/vfe97OeaYYzjqqKO4/fbb08cOBAJcd911rFixgrVr19LY2AjAH//4R4477jhWrVrF6aefnr59umgNR3lwfT03/vFN7nlhF7G4YWaxj7KAOx+CE2BlTMzKuD4TGBzKXw38WkR2AO8DfiAi7wUwxuxL/WwC7sOaMnL4iYC7IJVZMQuwwaaH4Imvw1+ugdfugaaNEB99hlK+me7vPRdffDG//vWvAfj9738/oF3me8ldd93FBRdcwFlnncXChQv53Oc+l26X+e3j7373O9atW8fTTz/NAw88wGc/+1lWrlzJ1q1b2bp1K2eddRbHHHMMJ510Ehs3bjygP3fddRfvfe97Oeecc6itreV73/se3/rWt1i1ahVr166lra0NgDvuuIM1a9awYsUKLrzwwvTfqv85AXjppZdYsWIFxx9/fPrD7GCf//zn+ec//8nKlSv59re/TW9vL5dddhnLli1j1apV/P3vf8/+xx3k7W9/O1/4whc45ZRTuPXWW3n00UdZtWoVy5Yt4yMf+QjRaBSw/qb977HHHnssW7ZsOaDfW7Zs4fTTT2fFihUcffTRbN26dcjX0o4dOzjiiCO44oorOOqoozjzzDPp6ekZ9jlSE0dECLgC1ARqKPeW80bLG/xk/U/4xgvf4Mk9T9Le257rLk55033M7udyubjlllvYtWsXr732GgDf+ta3WLp0KUuXLuU73/kOYI0RS5Ys4fLLL2fp0qV86EMf4pFHHuGEE05g4cKFWT87r1u3jk9+8pO87W1vY968eemgxEjOL/r19PRw1llncccdd2R9/jINfj56enqGHEOH8/jjj3PqqafywQ9+kGXLlg05nt91112cd955nHXWWSxevJgbbrghvY/M97NbbrmFZcuWsWLFCj7/eat2+lDj6lDP2VCvqbHKVQbFw8C1xpi4iHwNuBa4JrOBMWYTsBLShdv2Yn0wHrMb/vgGb+7rGo9dpR1ZU8B/nXPUkPe/8cYbHHPMMQNuKygoYPbs2ek38KH0f7jod++99zJ//vz0dY/Hw3333UdBQQEtLS2sXbuWc889l4ceeoiamhr+/Oc/A9DZ2UlhYSHf+ta3+Pvf/05ZWdkBffzqV7/KU089RVlZWfqDUr/3ve99fO973+Mb3/gGq1evxhjDf/7nf9Lc3Ex5eTk//elP0wPqwTzzzDO8/vrrlJSU8IlPfIJVq1Zx//3389hjj3HppZemI4Wvv/46zz77LN3d3axatYqzzz57wH5uv/12tm/fziuvvILD4Uj3+eqrr+bLX/4yAJdccgl/+tOfOOeccwDr25/nn3+eBx98kBtuuIFHHnmEiooKHn74YTweD5s3b+YDH/gAL744cLGCm2++mQ0bNqT7du+99/Lqq6/y2muv0dLSwpo1azj55JOprq4+6OOvq6vjkUcewW63pwe+U045hS9/+cvccMMN6YG3u7ubp59+mieeeIKPfOQjbNiwYcB+PvnJT3LKKadw3333kUgkCIfDANx5552UlJTQ09PDmjVruPDCCyktLaW7u5u1a9fy1a9+lc997nPccccdfPGLX+TEE0/k2WefRUT48Y9/zC233MI3v/nNEf0tp6pk0rCtpZt/bm5mw95O7CKUBty4HO5cd20ivAAsFJFarLH0YmDAV6rGmPT6TyJyF/AnY8z9IuIHbMaYUOr3M4EbD1vPhyICLp91AYhHYceTsP0fYHNA9XKYcQyULgT36NJbx91fPg8N68d3n1XL4F1Dp61O9/ee0047jSuuuIJEIsGvf/1rbr/9dm666aasbV999VVeeeUV3G43ixcv5hOf+ASzZs3K2vZtb3sb55577oAMg9NOO43bbruNhQsX8txzz/Hxj3+cxx577IBtN2zYwCuvvEJvby8LFizga1/7Gq+88gr/8R//wc9//nM+9alPccEFF3DFFVcA8MUvfpGf/OQnfOITnxiwn8suuyz93vHZz342az9vvvnmAdmL/eP6+vXr2bhxI2eeeSZ1dXV4PJ6s22fq6OjgH//4B729vSxcuJBHH32URYsWcemll/LDH/6QT33qU4D1+nr++efTj2XwN7of+tCH+PznP8/5559Pb28vyWQSl8uV9bUEsHnzZn71q19xxx138C//8i/ce++9fPjDHx7Rc6Qmjt1mp9xXDkBPvIe/7vwrD+14iPlF81lbvZb5RfOnfGHNrz3/NTa2HRhoHIslJUu45thrhrx/uo/Zmex2OytWrGDjxo3E43F++tOf8txzz2GM4bjjjuOUU06huLiYLVu28Nvf/pbbb7+dNWvW8Mtf/pInn3ySBx54gP/+7//m/vvvP2Df9fX1PPnkk2zcuJFzzz33gEyxoc4vwDopv/jii7n00ku59NJLuffeew94/oZ7Pnp7e1m3bt2QY+hwnn/+eTZs2EBtbe2Q43lmO5/Px5o1azj77LNZvXp1ej9/+ctfuP/++3nuuefw+XzpxzfcuJrtORvqNTXWOjU5yaAwxvzNGBNPXX0W69u84ZwGbDXG7JzYnk0cY0zWP9ZQt2c66aSTePXVV9OXzMGmfx9f+MIXWL58Oaeffjp79+6lsbGRZcuW8cgjj3DNNdfwz3/+k8LC4SvhP/bYY7zvfe9LD0QlJSXDthcRLrnkEv7v//6Pjo4OnnnmGd71rncNu02/M844I73/J598Mh19fcc73kFra2v6P/d5552H1+ulrKyMU089NR0J7ffII49w1VVXpdNN+/f597//neOOO45ly5bx2GOP8cYbb6S36Y/aHnPMMezYsQOAWCzGFVdcwbJly7joootGNK/4ySef5AMf+AB2u53KykpOOeUUXnjhhRE9/osuugi73U5nZycdHR2ccsopAPzrv/4rTzyxP3P+Ax/4AAAnn3wyXV1dB8zFe+yxx/jYxz4GWAN5/9/4u9/9bjpLYvfu3WzevBmwItL9cxczH/+ePXt45zvfybJly/j6178+4PnKN72xBM9vb+WWv27kh49vYXNjmJoiL9VFXlyO/ExRTY23V2OtzvEW8BtjzBsicpWIHCz3uhJ4UkReA54H/myMeWhiezwKDjcU1EDhLPCXQ+Ob8Pwd8JfPwT+/BduftGpZTLO6FdP9vcdut3PiiSdyzz330NPTw9y5c4fc72mnnUZhYSEej4cjjzySnTtH/pEjHA7z9NNPc9FFF7Fy5Ur+7d/+bcB0kkynnnoqwWCQ8vJyCgsL08HzZcuWpcfkDRs2cNJJJ7Fs2TLuvvvuA8bkwe8d/e+hB5P5frtkyRLmzJmT/kB7MO9///sB2LRpE7W1tSxatAgY+n3rAx/4AM8888yAfYRCIfbu3Zv+NtXj8eDz+YZ8LQHpOhgw8H3rYM+ROny8Di/V/mqq/FXsC+/j/976P25+/mb+sv0v7AvvG1AHRg1vuo/Zg/W/dp588knOP/98/H4/gUCACy64IJ0FXVtby7Jly7DZbBx11FGcdtppiMiAMXWw9773vdhsNo488sisWcNDnV+AdW5y2WWXcemllwIc8vN3sDF0OMceeyy1tbXp52So8fyMM86gtLQUr9fLBRdcwJNPPnnA47vsssvS03f6H99w42q252y4sXssJkMNio8AB8uNvxj41XANUoXbrgSYPXv2sDsbLtNhohx11FHce++9A27r6upi9+7dzJ8/n9bW1lHv++6776a5uZmXXnoJp9PJ3Llz6e3tZdGiRbz00ks8+OCDXHvttZx55pnprIJsRjL4DXbZZZdxzjnn4PF4uOiii0Y8L9Xv9w847mD9/Rjcn8HXs/W5t7eXj3/847z44ovMmjWL66+/fsD6u2639Q253W4nHrfiZN/+9reprKzktddeI5lMjujbpLG84WY+/uEc7PFn8/jjj/PII4/wzDPP4PP5ePvb355+/E6nM72PzMf/iU98gk9/+tOce+65PP7441x//fWH8GimhoGrcSQp9LqYWezLdbcOG2PMg8CDg27LVhATY8y6jN+3ASsmtHPjzeawghQAJgmhBqvIpgEClTD7OGtFkMJZh7duxTCZDhNF33usaR7nn3/+Qce1/vcGGDg+ZvZtqLXck8kkRUVFB8wTPthxbDZb+rrNZksfc926ddx///2sWLGCu+66i8cff3zAPkbznPVvN1r971sH20dmv7K9Z2cz1GsJDvy79E/xONhzpA4/m9go9hRTTDGxRIxn6p/hqb1PUeGr4Pjq4zmi9AgCrkmS0TYCw2U6TBQds/dLJBKsX7+eI444YsiAL4xsTB1um2zj0nCP8YQTTuAvf/kLH/zgBxGRUT1/o3Ww86d+ozl/guHH1WzP2XBj91hM2NeFIvKIiGzIcjkvo811QBy4e5j9uIBzgd8OdzxjzO3GmNXGmNXl5eXj9TDGzWmnnUYkEklXt00kEvznf/4n69atS0evRquzs5OKigqcTid///vf09/67Nu3D5/Px4c//GE+85nP8PLLLwMQDAYJhUJZ+/ib3/wmPfgNTtnKtm1NTQ01NTV85StfYd26daPq/8knn8zdd1svgccff5yysjIKCgoA+MMf/kBvby+tra08/vjjrFkzcPXDM888k9tuuy09ALW1taX/Y5SVlREOh0dU8Kazs5Pq6mpsNhu/+MUvSCQSB33sJ598Mvfccw+JRILm5maeeOIJjj320KbmFxYWUlxcnI4C/+IXv0h/Iwak57E9+eSTFBYWHhCVPe200/jhD38IWK+prq4uOjs7KS4uxufzsXHjRp599tkRPf4ZM6xFHX72s58d0mOYzBJJw+bGED/+5zZu/stGntjUTJHXxYxiH4Gpt1SoGg2xgbfYqltROAuSMdj4Z/jHLVZ2xSt3Q8MGiPXkuqcTQt97rG8Vr7322vQ3+4eqsrKSt956i2QymS78PLhPBQUF1NbW8tvfWh9VjDHpedOjEQqFqK6uJhaLpd8fMxUVFVFYWJj+Vixbm8F9hIHvt3V1dezatYvFixcfUt+WLFnCjh070unmQ71v3XPPPRx//PEDti0oKGDmzJnplOtoNEokEhnytTScgz1HKrf6C2tW+6vpjffyh61/4GsvfI1fbfwVm9s3E09mP3Gc7nTMtsRiMa699lpmzZrF8uXLOfnkk7n//vuJRCJ0d3dz3333cdJJJ43maRiRbOcX/W688UZKS0vTRfOHev4yZT4fBxtDR2q48fzhhx+mra2Nnp4e7r//fk444YQDHl9mXZP+x3eo4+poxu6RmLBP6MaYYdekEpF/Bd4DnGaGDyW9C3jZGDOlq/aJCPfddx8f//jHuemmm0gmk7z73e/mv//7vwGrLkJmZCrT4DllX/ziFwfMlfrQhz7EOeecw+rVq1m5ciVLliwBrDlJn/3sZ7HZbDidzvSJ7JVXXsm73vUuqqurBxTIOuqoo7juuus45ZRTsNvtrFq1irvuumtAX9atW8dVV12F1+vlmWeewev18qEPfYjm5uZRVzK//vrrueyyy1i+fDk+n2/ACfKxxx7L2Wefza5du/jSl75ETU3NgHStyy+/nLq6OpYvX47T6eSKK67g6quvTk/XmDt37gFBjWw+/vGPc+GFF/Lb3/6WU089NWuGQ2lpKSeccAJLly7lXe96F7fccgvPPPMMK1asQES45ZZbqKqqOuTH/7Of/YyrrrqKSCTCvHnz+OlPf5q+r7i4mLe97W10dXVx5513HrDtrbfeypVXXslPfvIT7HY7P/zhDznrrLO47bbbWL58OYsXL2bt2rUH7cP111/PRRddxIwZM1i7di3bt28/5McxmUT64ry2u5PHNjbS1t2H12mnpigvVuNQYyECroB1Aatuxe7nYedTgED5IpixxvrpK7XaT3H63mM9B4NXUDoUN998M+95z3uYNWsWS5cuTdf6ufjii7niiiv47ne/y+9+9zvuvvtuPvaxj/GVr3yFWCzGxRdfzIoVK3jggQd48cUXufHGkZduuemmmzjuuOOYM2cOy5Yty3qS8NOf/pSPfOQj+Hw+3vnOd2bdz/Lly3E4HKxYsYJ169bx8Y9/nKuuuoply5bhcDi46667hvz7D8Xj8fDTn/6Uiy66iHg8zpo1awZU749Goxx33HEkk0l+9asDk19/8Ytf8G//9m98+ctfxul08tvf/nbI19JwRvIcqdzrL6wZcAVImARb2rewoWUDPoePY6uPZXnZcip8FWOes54vpvuY/aEPfQi32000GuX0009PF108+uijWbduXfqLwMsvv5xVq1YNOYVjrIY6v+j3ne98h4985CN87nOf47TTTsv6/GUa/HwMN4aO1HDj+Yknnsgll1zCli1b+OAHPzig/gTAWWedxauvvsrq1atxuVzp19ihjqujGbtHQnIxL0xEzgK+BZxijGk+SNtfA381xvx0uHaZVq9ebQYXOHzrrbc44ogjRtPdw+LWW29l79693HLLLbnuyiG7+uqrWbVqFR/96EfHdb9DLc05Xbz97W9PF9SZDCb7/yFjDA1dvTy7tZXntrcRTyYp8rrwu8c/Dlvf2cMZR1ZyxpGHFpASkZeMMZPjDzrBso3DB7XtH/Dar6BozsR0ajjJBPR2QixsTQXxl8OsNVBxpNUf++heR5P9/42+96jxNHfuXF588cUDiurlSrb/f9NpHIZDH4uNMXzxqS8yIzBjAnsF0USU9t52kiZJtb+atTVrWVKyBL9zZFNgJ4qO2RNHx+zD46677uLFF1/ke9/7Xq67knaoY3Gucpy/B7iBh1MR02eNMVeJSA3wY2PMuwFExAecAfxbjvp5WHz0ox9lw4YN/OY3v8l1Vw7ZMcccg9/vz/sVH9TkFUskqWsM8fjGZra1hHHYbJQFXDjs+VnwUk0Qmx18JUCJVUgzFoFNf4VNfwG701oto2YVlMwHb1Guezsu9L1HKZUrbrubKn8VxhjCsTD3bb4Pm9g4ovQIVleuprawFodNp2Jm0jFbTRc5+Z9vjFkwxO37gHdnXI8ApYerX7nyk5/8JNddGLWXXnppwvadj4UaD4UW/BpeZyTGy7vaeXxTE93ROD63gxlFXk0TVWMnAi6/dQFIxKxVQfa+bAUvCmfsnwpSOGvU2RW5pu89arxNVLq1yl8iQtAVJOgKDpgC4nV4ObbqWJaXL6fSV6nv7eiYrUZm3bp1o64LOFlMzU9VSqlpyRjDztYIT21p4dXdHRig1O+iyDe111pXk5zdmbEqiIG+MLz1ALxlwO6CquVQswJK5lkFOZVSagipac63AnasrOGbB91/HnATkMQqJP8pY8yTI9l2qrOLnVKv9b1kNBHlyb1P8sSeJ6jwVbC2ei1LSpdQ4CrIcS+VUhNNAxRKqUmvN5Zg/Z5OHt/URENXFJddqCzwYLfpNyrqMBMBd9C6gJVd0bAe9rxg3ReoghnHQPliKBp+yWul1PQiInbg+1jTl/cAL4jIA8aYNzOaPQo8YIwxIrIc+A2wZITb5o3MKSDdsW4e2PYAf9z2RxYWLWRN1RrmF83HZdcvJ5TKRxqgUEpNWo1dvTy/vY2nt7YQiycp8DqZUeTRVE81edidEKiwfjcG+rqtuhWb/mzVtZh9CUTD4HCDzZEXK4MopUbtWGCLMWYbpAvBnwekgwzGmHBGez9W2d4RbZuPMlcBSZoku0O72dS+CZfdxaqKVawsX8nM4ExsonWnlMoXGqBQSk0q6aKXm5rZ1tyNwyaUBlw4teilmuxEwB2wLgDJuHWJWGvFY7OD0wsOLzhcVsBCKTWdzAB2Z1zfAxw3uJGInA/8D1ABnH0o26a2vxK4EmD27PzJ5LKJjWJPMcUUE0vGeLnxZZ6vf56gK8hx1cdxVOlRlHnL9EsMpaa4afvp6Jt/28S+jp5x219NkZf/PHPxsG0+8pGP8Kc//YmKigo2bNgwZLtbb72VO+64A2MMV1xxBZ/61KcAeO2117jqqqsIh8PMnTuXu+++m4KCAp566ik+9rGP4Xa7+dWvfsWCBQvo6Ojg/e9/Pw899FDWgToWi/GlL32Je++9F7fbjc/n44YbbuBd73rXpFsmTE0P7d19vLSrjSfqWoiki15qtoSawmwOEJtVpwKAJDz+NQjVW1fFZgUtpP8yimMUzoR3fHHYJvreo9Skke1/uTngBmPuA+4TkZOx6lGcPtJtU9vfDtwO1jKjo+7tJOa0OanwWdlrPfEeHt31KI/sfISaQA3HVh3LkpIlBFyBMR/ne698j/ru+jHvp1+1v5qrV109bBsds9V0d9AAhYi8CPwU+KUxpn3iu3R47OvoYWaxb9z2t6c9ctA269at4+qrr+bSSy8dss2GDRu44447eP7553G5XJx11lmcffbZLFy4kMsvv5xvfOMbnHLKKdx55518/etf56abbuKb3/wm9957Lzt27OCHP/wh3/zmN7npppv4whe+MOTJ3Ze+9CXq6+vZsGEDbrebxsZG/vGPf4z68Ss1GsmkYVtLmH9ubuGNvV2IQInfRbEWvRxSvo7J04MNwo3Wyh8AGGtaSP85hs0BNqcVtLDZGVHEomPnQZvoe49S42sM4/AeYFbG9ZnAvqEaG2OeEJH5IlJ2qNtOJ16HF6/DizGGUF+I+7fcjyAsKl7E6qrVzCuah9vuHtW+67vrmRGYMW593Rvee9A2Omar6W4kOdMXAzVYxXh+LSLvFP1Kc1ROPvlkSkpKhm3z1ltvsXbtWnw+Hw6Hg1NOOYX77rsPgE2bNnHyyScDcMYZZ3DvvfcC4HQ66enpIRKJ4HQ62bp1K3v37uWUU07JeoxIJMIdd9zB//7v/+J2WwN2ZWUl//Iv/zJeD1WpYYV6Yzy5uZmvPvgmP3x8K5sbw1QXeqgp8uJx2nPdvclOx+S8IVYWRX8GhUlCvMdaJaS3y/oZj1rTRLJ/UToi+t6j1Lgb7Tj8ArBQRGpFxJXazwOZDURkQf++RORowAW0jmTb6a5/ydKaQA2V/kp2hXZx91t38z/P/Q+/3/x7tnVuI56M57qbB6VjtpruDppBYYzZAlwnIl8C3gPcCSRF5E7gVmNM2wT3cVpZunQp1113Ha2trXi9Xh588EFWr16dvu+BBx7gvPPO47e//S27d1tTEa+99lquvPJKvF4vv/jFL/jMZz7DTTfdNOQxtmzZwuzZsyko0KWa1OHTv0ToM1tbeWV3O8kkFPmc45rJNB3omJzPxApU9OsPWPTfZ7NbRTnlEDIsRkjfe5QaudGOw8aYuIhcDfwVa6nQO40xb4jIVan7bwMuBC4VkRjQA7zfGGOArNtO7COdujLrVcSTcdY3r+flxpfxOrysrlzNsvJlVPurp+w0Uh2zVT4bUQ2K1DJHlwHvBu4F7gZOBB4DVk5U56ajI444gmuuuYYzzjiDQCDAihUrcDisP9Odd97JJz/5SW688UbOPfdcXC4rDX7lypU8++yzADzxxBPU1NRgjOH9738/TqeTb37zm1RWVubsManprTsaZ8PeTh7f1ExzKIrTIVQEdYnQsdAxeboYFLBIJlLZFKn7bHZrmdN4NBW4GH0hWX3vUerQjHYcNsY8CDw46LbbMn7/GvC1kW6rDs5hc1DuKwcgmojy1L6neGLvExS7izmu+jiOKD2CUk/plApW6Jit8tlIalC8BHQAPwE+b4yJpu56TkROmMC+TQu7d+/mnHPOAeCqq67iqquu4qMf/Sgf/ehHAfjCF77AzJkzAViyZAl/+9vfAKirq+PPf/7zgH0ZY/jKV77CPffcw9VXX80NN9zAjh07+O53v8tXv/rVdLsFCxawa9cuQqEQwWDwcDxMNc0YY9jT3sOz21p5cWc7iWSSQo+LGi16OWY6Jk9jgwMQJgmJPgg1WMkUdre1SojdbQUsbENPl9L3HqVGT8fhqcttd1PlrwIgEovw1x1/5a87/kqVv4pjq47Fb/w57mF2Omar6WQkGRQX9a+53E9Eao0x240xF0xQv6aNWbNm8eqrrw64rampiYqKCnbt2sXvf/97nnnmmQG3J5NJvvKVr3DVVVcN2O5nP/sZZ599NsXFxUQiEWw2GzabjUhkYAFPn8/HRz/6UT75yU/yox/9CJfLRX19PY8++igf/vCHJ/TxqvzW05ewsiXqmmno7MFpt1Hmd+HIsyVCo/EEiWTOCqPrmKxSJGOVEGNlV/R07L/b7gSHx7okBs671vcepcZEx+E84HP68Dl9GGPojnXzwNYHONt/Ns2RZvxO/6gLa04EHbPVdDKSAMXvgKOz3HbM+Hfn8Kkp8o5o5Y1D2d/BfOADH+Dxxx+npaWFmTNncsMNN6Qjn5kuvPBCWltbcTqdfP/736e4uBiAX/3qV3z/+98H4IILLuCyyy5LbxOJRPjZz36Wjph++tOf5sILL8TlcvGrX/3qgGN85Stf4Ytf/CJHHnkkHo8Hv9/PjTfeOKrHrqa3/myJ57a38uKOduIJQ4HXwYwib15lS0TjCbY1d1PXGGJnW4TasgBnLa3ORVfyckyeNoLV0HXwKu6HtD8gPR3EPqiGRV+YD1yyjsefepaW1nZmzqjhhi9dx0cvv/yAaSH63qPUiOk4nEdEhIArQMAVwC524sk47b3tIFDoLmRX1y5sYhuXzzTV/oN/btDzBTXdiVV3J8sdIkuAo4BbgM9m3FUAfNYYc9TEd290Vq9ebV588cUBt7311lscccQROeqRUlPf4P9DkT6rtsQ/6lpo6OzBkYfZErFEkh0t3WxqDLGjNUIiaQh6HNQUebj0+Ll86Lg5h7Q/EXnJGLN6NH2ZamNytnH4oLb9A177FRQd2vM6mb1VdCpHzJ8sj8dYdSxMcv9Ndhc4PRnTQhyQR4FFNb1l++w3ncZhOPSx2BjDF5/64rgurTmVrJbVzF04N309kUyQNEkQ8Ng9+Jw+3HY3tjHU/FFqujnUsXi4DIrFWJWJi4BzMm4PAVeMrZtKqanIGMOutgjPb2+zakvkYbZEPJlkV2uEusYw21rCxBIGn8vO0poCFlcFqSrw0NDVS0XwsKd+6pisxkisAEQmk4BoCExXqokNHC5rWojdtX/VkDz5/63UGOk4PM3YbXbs2DHGEE1E6Y33goDX4cXn8OGyuzRYodQ4GzJAYYz5A/AHETneGPPMYeyTUmqSSSSTRGMJbnloE82h3rzLlkgmDXs6etjUEGJrc5hoPInHYWNxZZBFlUFmFHux5fgETcdkNSHEPnClEIy1MkisN6ONDRzuVNDCqUELNW3pODx9iQgOsU6bjDH0xnvpifUgIngcHg1WKDWOhgxQiMjnjDG3AB8UkQ8Mvt8Y88kJ7dkEMMbkzbe8Sk00YwzReJLuaNy69CXoi9uoyZNsCWMM+zp7qWsMsbkxTE8sgctuY165n0WVQWaX+CbVUqhjHZNF5CzgVsAO/NgYc/MQ7dYAzwLvN8b87lC2VdmYKfbek6WWBcZayjTWk9GsP2jhtjItbA6dHqImlaGmMI9FPn42VgOZEYzZwwUrvA4vXodXgxVKpYxmLB5uisdbqZ+HOIn44ETk61ipcX3AVuAyY0xHlnb/AVwOGGB9ql3v4HYj4fF4aG1tpbR0aq1zrNThFk8k6YklCPXGUytVGKLhTnqTNgKekdTVnbyMMTSFotQ1hqhrDBOOxnHYhNoyKygxt9Q3mbNCRj0mi4gd+D5wBrAHeEFEHjDGvJml3deAvx7qtio7TzxMa1c3pQX+Kfzek2VqCMZa4rQ/aCGpf+xOq56Fw50RtLBp4EIdVsYYWltb8Xg8473rCftsrCaHCBHCHWECRYERjdmDgxU98R4isYhmVijF6Mfi4aZ4/DH182dj7Fs2DwPXGmPiIvI14FrgmswGIjID+CRwpDGmR0R+A1wM3DWaA86cOZM9e/bQ3Nw8tp4rlYeMMcSThmgsSSxhFdCz2YT+t+ZwXHiry5m7Do5RS3h/UKKzJ4ZNYG6pnxMXlFFb5sflmPwfHMY4Jh8LbOlfFk9Efg2cBwwOMnwCuBdYM4ptVRYzu19nTwM0twSAaXCSbpJgrMCm9XiNFaCw2VPBCgfYbKmgxeT/f6emLo/Hw8yZM8d1nxP82VhNApuTm6EFfC0+ZAxjtsFYxTVTQ6HT5sRld+EQxxQOVit16EYzFh/061AReRhrveeO1PVi4NfGmHeOppMAxpi/ZVx9FnjfMP3zikgM8AH7RntMp9NJbW3taDdXKu8YY2gORXlpZztPbW2hJ5bE67BR7PfkvN7CeOiI9FHXGKauMURrdx8CzCrxsWZuMfPLA3ic9oPuYzIa5Zg8A9idcX0PcNyg/c4AzgfewcAAxUG3zdjHlcCVALNnzx7Bo8l/TtNHbXgaf9lq+jMtItAXAZKAzSrO6Q5C4Qxr1ZaCGeArsS7uQiuIodQkNRGfjdXkEJc4b5m3Dt5wpATiyTidPZ1EE1FsYmNxyWJWlq+ktrAWn9M3fsdSKk+MJF+7PHP6hTGmXUQqxrEPHwHuGXyjMWaviHwD2AX0AH8bFNgYQD8YKzUyPX0J3qrv4p+bm9nV1oNdoCTgotQ/NU/YM3X1xticCko0haIA1BR5ePvichaUB/C7p/YUlZTRjMnZIk6DJwV+B7jGGJMY9O3OSLbt78vtwO1gLW13kD6p6UBkf60Kb/H+2/sDF537oGULmLiVUWGMVQMjWGkFLQpngr/M2tZbDO4CnS6iJoOJ/mys8ojD5qDUWwpYy5Zu79zOW61vISLMK5zHqopVzCuaR4GrIMc9VWpyGMmn9YSIzDbG7AIQkTkM8eE0k4g8AlRlueu6VBVkROQ6IA7cnWX7Yqw04lqgA/itiHzYGPN/2Y6nH4yVGloyadjZFuH57a28squDWCJJ0ONkRpFnyqcadkfjbGkKs6kxRH2nVaKmIujmpIVlLKwIEPRM3akpQxjNmLwHmJVxfSYHZqStBn6dej2UAe8WkfgIt1Xq0AwIXBQNvC+ZsGpbNKyHPS8ycLqIHQLlEKiCgmrwV4CncP/F6dUAhjocRvXZWCm7zU6JpwSApElS313P1rqtAMwMzmRVxSrmF82n1KM189T0NZIAxXXAkyLyj9T1k0llKgzHGHP6cPeLyL9irSV9msle3vN0YLsxpjnV/vfA24CsAQql1IHauvt4bXcHT25ppjMSw+mwUZoHy4P2xhJsabYyJfa09WCAUr+L4+eVsqgyQJHPlesuTqTRjMkvAAtFpBbYi1XP54OZDYwx6TlwInIX8CdjzP0i4jjYtkqNK5sdXAHrMlgyAfFeaKmDhtetqSJiA8Sqf2F3ga/UyroIVIK/HDwF1nQSd4G1T4dbgxhqrEb12VipTDaxUeQuoshdhDGGzmgnf9z6RwBKvaWsqljFouJFVPmrtMimmlYOGqAwxjwkIkcDa7G+wvgPY0zLWA6aWrLuGuAUY0xkiGa7gLUi4sOa4nEaWjVZqYPqjSXY3BjmyS3NbG0KgwjFPicziqf2PMe+eJJtLWHqGsPsbO0maaDQ62TN3BIWVQYoDbhz3cXDYjRjcqog8dVYq3PYgTuNMW+IyFWp+2871G3H6eEodWhsdnD5rUs2ybi1JGrrNmh6y7reP0tJsIIYNpeVteEttoIZ3hLwFYPTlwqM+FK/+61VSVTumIzVYuJRiPfsX/I23mv9PWcff9gDThPx2VhNbyJC0BUk6AqmVwN5dOejPLrrUXwOHyvKV7CkZAmzgrNw6rik8tyQAQoRWWKM2ZgagGF/Su/sVFrby2M47vcAN/BwKn3pWWPMVSJSA/zYGPNuY8xzIvI74GWsaSCvkJrCoZQaKJk07GqL8NKudl7c0UZfPInf7aCmyDulUwTjiSTbW7upawyzvaWbRNIQcDtYOauIRZVBKoLuKf34DsVYx2RjzIPAg4NuyxqYMMasO9i2Sk1KNge4HEMHMMAKWiRiEKqHjl3WCXAyMXA5VGNSGRnOVPZFcH8WhqfAKuTp9ILTAw6PlZXRv7yqw21lctgc0zdTI5mwnuNEX8bPqPV7PJq63gexXugLQV/3/kus2yqoGotY95PMyJIBxOz/+xgDM46xnvPDYII/GysFWMEKn9OXLqAZTUR5vv55nql/BrvYWVKyhGVly5hTMIdAtkwzpaa44TIoPo2VrvbNLPcZrErvo2KMWTDE7fuAd2dc/y/gv0Z7HKXyXWs4yut7OnlqSwsdPTEcNqHE78I5hadwJFLBlrrGEFubw8QSBq/TzlE1BSyuDFJdOPXrZozShI3JSk0rNod1cXqHb9d/EpyMQW8XRNqsE+xk3LoIA5dKNak6GaROoMWWEcBIBTH6f7e7rPvsrowAhytjOVZ7ajlW+/5lWtNLs4oV+BDZ/3tmLVuR1FKv6Y7tX/rVZP6eTD2+xP7fTQKS/T8T1mNPxCDeH2DICDKkAw3R/ff3356MAxkBn3QWi0lVakgdHxn0mB37r7sLwVs6fJCnc/fQ900MHYfVYee2u6nwWzVY48k4m9s3s6FlAwCzgrNYUb6CeUXzKPeWT9fPRyrPDBmgMMb0z6V7lzGmN/M+EfFMaK+UUkPq6UuwsaGLp7e0sr21GwGKfE5mFB3kw/YkljSGve091DWG2NIUpjeexO2wsagyyKLKIDOLvNhs0/tNV8dkpQ4zkdRJs90KIhyq9Ml/Yv+0hHQwIDMokLSCAv01FqX/n8ygw6BfjMloZ1LBkYGbpEs2SkaR0ewNUvvIuM0k998nsj84kvWSCqbYnOD2gNe+f7s8o+OwyrXMFUGMMbRH2/nTtj8hCAFXgOXly1lcvJiZwZm47Hldj0vlsZEUyXwaOHoEtymlJkg8kWR7Szcv7mjn1T0dxBNJAm4HNVM4m8AYQ31nL3WNITY3hYn0JXDahXnlARZVBphT4sc+zYMSQ9AxWampQGxgtwE6XzwP6Tisck5EKHAVpJcn7Y338uy+Z3l639PYxMaCogXpqSBF7qIp+3lRTT/D1aCoAmYAXhFZxf6wewEwtavtKTUFGGPY19nLa7s7eHZba/oEvizgwmGbmlM4jDE0h6LUNYapawoR6o1jtwm1pX4WVQaYW+af0tNTJpKOyUoplVvjMQ6nCsXfilV0+MfGmJsH3f8hrELyAGHgY8aY11L37QBCQAKIG2NWj+kBqbzicXjwpLK9EskEu7p2saltE2CtCrKsbBkLihYwIzBDC22qSW24DIp3Auuw1rz/JvsH4RDwhYntllLTV3t3Hxv2dfL0llaaw73YxKorUTyFl85sDaeCEo0hOnpi2ARml/g4fl4p88r9uB32XHdxKtAxWSmlcmtM47CI2IHvA2cAe4AXROQBY8ybGc22Y61y1y4i78IqEH9cxv2n6ooh6mDsNjvFnmKKKU6vCvLEnid4Ys8TiAgLihawtHQpswpmUeop1ewKNakMV4PiZ8DPRORCY8y9h7FPSk073dE4mxpCPLOtle3N1tKgRV4nNYVTdxWOzp4YdY0hNjWGaA33ATCz2Msxc4qZXxHA69SgxKHQMVkppXJrHMbhY4EtxphtACLya+A8IB2gMMY8ndH+WaxgyGHTHevGDCiyqqa6wauCpLMr2jeBgUJ3IUeVHsWC4gXMDMxMt1MqV0ZSg2KmiBRgRYfvwJpf93ljzN8mtGdK5bloPMG25m6e397KG/u6SCbNlF8aNNwbp64pRF1jiMauKADVhR5OWVTOwooAfvdIhhx1EDomK6VUbo12HJ4BZC49soeB2RGDfRT4S8Z1A/xNRAzwI2PM7dk2EpErsVYbYfbs2Qfp0kD/+fh/8lLTS8wIzKDaX02Vv4oybxk20emX+WJwdkU0EeW5hud4Zt8zAMwMzuSosqOYUzCHan81Dpt+dlOH10hecR8xxtwqIu8EKoDLgJ8C+mFYqUOUSBp2tHbzyq52Xt7ZQV8igdthpyLombIFISN9cbY0halrDLO3oweA8qCbExeUsbAiQIFX5zmOMx2TlVIqt0Y7Dmd7o8+ariAip2IFKE7MuPkEY8w+EakAHhaRjcaYJw7YoRW4uB1g9erVh5QO8a7ad7EztJPmSDPbOrcB1soRlb5KqvxVVPurqfRV6goReUJEBtSuSJokHdEO/rr9ryBgFzvzi+ZzRMkRzC6YrcEqdViMJEDRP5i+G/ipMeY1mapf7yqVA8mkYW9HD6/v6eC57W1E+uI4bDZK/C6cdneuuzcq0ViCLc1WUGJ3ewRjoMTnYu28EhZVBqd0vYwpQMdkpZTKrdGOw3uAWRnXZwL7Dti5yHLgx1jLmbb2326M2Zf62SQi92FNGTkgQDEW5y04jxcaX2BGYAbhvjAN3Q00RBqoD9fzcuPLGAyCUOoppcpflQ5aBFyB8eyGyhGb2Ai6ggRdQQDiyXi62KaI4LF7WFSyiCXFS6gJ1FDiKZmyWb9q8hpJgOIlEfkbUAtcKyJBIHmQbZSa1owxNHZF2bCvg2e3ttHR04dNbJT4nFP25D2WSLKtuZu6xhA7WyMkjKHA4+CY2cUsrgpS6nfpm9ThoWOyUkrl1mjH4ReAhSJSC+wFLgY+mNlARGYDvwcuMcbUZdzuB2zGmFDq9zOBG8fl0Qwh4AqwwLWABcULAOhL9NEYaaS+u56G7gY2tm9kQ+sGq60zkJ4SUu2vpthTrN+05wGHzZGeDgLWa2Bj60Zeb3odBPxOP4uLF7OoeBE1gRpdzlSNi5EEKD4KrAS2GWMiIlKKlcqmlBqkJRzlzX1dPLutlcYuawWOIp+TGUVTs+BQPJFkZ1uEuoYQ21q6iScNAbeD5TMLWVQVpDLo1jeiw0/HZKWUyq1RjcPGmLiIXA38FWuZ0TuNMW+IyFWp+28DvgyUAj9Ivb/2LydaCdyXus0B/NIY89C4P7JhuOwuZgVnMStoJYEkTZLWntZ0wGJveC+bOzZbbW0uKv2V6aBFha8Cp02nfE51LruLUm9p+no0EWV9y3peanwJgKAryOKSxSwoWkBNoIZid7F+TlSH7KABCmNMUkS2A4tExHMY+qTUlNLW3cfGeisosa+zBxAKvU5mTNFil4mkYXd7hLrGEFubuulLJPE67RxRXcDiyiA1RZ4p+bjyhY7JSimVW2MZh40xDwIPDrrttozfLwcuz7LdNmDF6Ho8MWxio9xXTrmvnOXlyzHGEOoLWVNCuuup767n+YbnrbbYKPOVUeXbPy1EV4uY+tx2N27v/unK0USU15tf58WGFwHwOX0sKl6UDlhoDQs1EgcNUIjI5cD/w5on9yqwFngGeMeE9kypSawzEuOt+i6e297K7rYICAQ9U3dZ0KQx7OvoYVNjiC1NYXpjSVwOGwsqAiyqDDCr2IdtihbxzDc6JiulVG7pOJydiFDgLqDAXcCi4kUA9MZ7B0wLeaP1DV5veR2AQlfhgDoWOj1g6hscsOhL9PFW61u82vwqguC0OaktrGVh0UJmBGdowVWV1UimePw/YA3wrDHmVBFZAtwwsd1SavLpjMTY1NjF89vb2dHSbQUlpvCyoMYYGrp6qWsMs7kxRHdfAodNmFfuZ1FlkDmlPhw2jXKD9VxF40m6emLEkgZjDH5Xzpbd0jFZKaVyS8fhEfI4PMwpmMOcgjkAJJIJmnuaaei2six2du1kU/smq63dM3BaiLcCu82ey+6rMRo8JSSejLM7tDtddBNgRmAGC4sWMqtgFpW+SgpcBVPyc7UaPyP5hN1rjOkVEUTEbYzZKCKLJ7xnSk0CmUGJna3dGCDgdkzZaQ7GGFrCfdQ1hqhrDNHVG8duE+aW+lhUGaS2zI/TrkEJgGg8QVdPnFgiicFQ4HGyem4Ji6uCzCr2UeDNWYBCx2SllMotHYdHyW6zp7MmVrISYwydfZ3Uh+vTU0N2du0ErCkkFd6KdIZFlb8qvRymmpocNgdF7iKK3EWAVcekK9rFE3ueIEkSYwxBV5DawlrmF86nKmAFqpx2rV8ynYzkE/YeESkC7sdac7mdLEsiKZUvOiJ9bGoI8fyONna2RABDwOOkunBqBiUA2rv72JQKSrRHYojA7BIfx9WWMr/Cj9uh31DEElaGRG/cKsTud9lZPrOQI6oLmFXio9jnnCx/fx2TlVIqt3QcHicikj5hPaL0CAAisciAaSGvt7zOq82vAlDsLh4wLUS/bZ/abGIj4Aqkl6k1xtCX7GNT2yZeb349/bet8dcwr3AeswtmU+Gr0FVi8txIimSen/r1ehH5O1AIHNaqwUpNtLbuVFAiXVNCpnSmBEBXTyyVKRGmORwFYGaRl1Wziplf4ceXuykKk0I8mSTUGyfSl0AAl8PGkqoCjqwOMqvUR3lgcq5QomOyUkrllo7DE8vn9FFbWEttYS1gTQtoijSlp4Vs7dzKW21vAeB1eAcsb1rqLcUu+qXLVCUiB9SxSJokob4QT+97mif3PgkCTpuTWcFZzCucR02ghgpfhQar8siQZygiUpLl5vWpnwGgbbQHFZGvA+cAfcBW4DJjTEeWdv8PuAIQ4A5jzHdGe0ylMhljaA5HU0GJNuo7e4GpXVMCIByNszkVlGjosh5TVYGHkxeWsbAySMA9fYMSyaQhHI0TjsZBwC7CgooAS2sKmVPmozLomdSFQCdyTFZKKXVwOg7nhsPmoCZQQ02gBrA+w7X1ttHQ3ZCeFrKtc1u6bYW3Ih20qPRX4ra7h9u9muQGZ1mAFbRq6G5gW8c26yzRgNvhZnZwNnML51Ltr6bcW06hu3DKfqafzoY7W3kJMFh/9n791w0wbwzHfRi4NrUe9NeAa4FrMhuIyFKs4MSxWIGMh0Tkz8aYzWM4rprG+otCbqy3pm80h6ysggKPg5opPH2jpy/BlqYwdY0h9nT0AFAWcHHC/FIWVQYp8E7PeXvGGCJ9Cbp6YxgDIjCnxM/bl5RTWxqgusgz1eptTOSYrJRS6uB0HJ4ERIRSbyml3lKO4igAwrGwFbBIZVm83PQyBgNAqad0QB2LoCuYy+6rceCwOSh0F1LoLkzfFkvE2Bvey+aOzQiCMQan3cq0mFMwJ73MabG7WIuvTnJDBiiMMbUTdVBjzN8yrj4LvC9LsyOwqiNHAETkH8D5wC0T1S+VfxJJw972Ht6s7+SFHW109sQRoNDrnNJBiWg8wdbmbuoaQ+xqi2AMFPucHFdbwqLKICX+6blkU28sQWdPjHjSAIbKAg+n1VYyvzzAzGIvHufUfUOayDFZKaXUwek4PHkFnAEWFC1gQdECwDpZzaxjUddexxutb6TbZtaxKPGUaD2DPOC0Oym0DwxaxJNxGrsb2d65HWMMCAhClb+K2cHZzAzOtIJdnlJ8Tl8Oe68yDTfF48PGmP9L/X6CMeapjPuuNsZ8b5z68BHgniy3bwC+KiKlQA/wbuDFYfp7JXAlwOzZs8epa2oq6osn2dXWzYa9nby8q4NIXwKbCEVeJzOKvLnu3qjFEkm2t1hBiR2tERJJQ4HHwdGzi1lcGaQs4JqyAZfRiieSdPbE6I0lQIQCj4M1tSUsqQoyq8RHgSd/skcO45icG7EeCDeDSea6J0oplVXej8N5xGl3MjM4k5nBmYBVx6C1pzWdYVHfXc+Wji1WW5uTKl9VOmhR6avUVSPyhMPmoMBdQIG7IH1bwiTojnXzUuNLPFv/rJVtgSHgCjAjMIPZQasQZ6m3lGJPMU6bvhYOt+GmeHwa+L/U7/8LHJ1x30eAYQdhEXkEqMpy13XGmD+k2lwHxIG7BzcyxryVmv7xMBAGXku1zcoYcztwO8Dq1avNcH1T+SfSF2dbczev7u7gjX1dxBJJ7CIU+50U+6ZuNkE8mWRXa4RNjSG2t3QTSxj8LjvLZhSyuDJIZcHkLOQ4UZLGEO6NE+qNIwJOu40lVUGOqilgdqk/34M0Yx2TzwJuBezAj40xNw+6/zzgJiCJNdZ+yhjzZOq+HUAISABxY8zqsT6YA+x9CX7/UUDAHQR3AXgKBv7s/12XmVNK5caYxmGVOzaxUe4rp9xXzrLyZRhjCMfC6QyL+u56Xmh8AbC+YS/zlqWnhFT5q/A7/Tl+BGq82MWO3+kf8Dc1xhBLxtgd2s3mts0YDCJCkiSlnlJqAjXMDMykzFtGiaeEIk+RBi4m0HABChni92zXD2CMOX24+0XkX4H3AKcZY7IGFIwxPwF+kmr/38Cegx1XTR8dkT62NIV5eVc7mxvDGAwuu50SnxPH1KotMEAyadjdHqGuMcyW5jB98SQep43FlUEWVwWpKfJiy9+T8AGMMfTEEnT1xkkmrWFiTqmPU5eUU1sWoLrQM6X/1odo1GOyiNiB7wNnYI2jL4jIA8aYNzOaPQo8YIwxIrIc+A2wJOP+U40xLaPu/cGUzIO1/w7bHrcKhvR2Qtc+iG48MKvC7j4waJH5uysAOr9UKTX+xvTZWE0eIkLQFSToCrKoeBEA0UQ0XceiobuBN1vf5PWW1wEocBUMqGNR7C7O5y9Eph0RwWV34bK7IKOmqjGG3kQvm9s3s75lPZLx37zYXZwu3lruLafYU0yRuwiPfokyZsMFKMwQv2e7fkhS3+RdA5zSX2NiiHYVxpgmEZkNXAAcP5bjqqnNGEN9Zy91jSFe2tlOfaogpM/toKpgcq/AcDDGGPZ19LKpMcSWpjA9sQQuu4355X4WVQWZVezDPoUf36GIJZJ09cTojSfACCV+FycuKGNx6nnwuqbtiedYxuRjgS3GmG0AIvJr4DwgHaAwxoQz2vtHsM/xVVADi94JPW1QNGf/7cZAXzdEu6ygRbQr9XvqZ9c+iPcM2pmAOzB0AMNdCA63FQhRSqmRm7DPxir33HY3cwrmMKfAeg9KJBO09LRYK4WE69kV2kVde126bea0kApfBQ7b9F0pLV+JCF6HF69j4BTx/sDF1o6tvNHyRrq2RdIk8Tv9VPor08GsQnchxe5iClwFWpxzhIb7n7RERF7HigjPT/1O6vpYqxR/Dys+9XAq+visMeYqEanBSj1+d6rdvakaFDHg340x7WM8rppirHoSEd6q7+KVXe109VpFLoOeqb0cKFiDW2MoSl1jiM2NYcLROA6bMK/Mz8LKIHNLfdMiO8AYQygaJ9xrzeBy2m0sqQ6ytKaQuWV+in3OKf13HkdjGZNnALszru8BjhvcSETOB/4HqADOzrjLAH8TEQP8KDWl7gATUgtI+oMNASuIkU2iD6KhVAAjNDCQEaqHljowiYHb2F1DBC/6L0HNwlBKDTaRn43VJGO32an0V1Lpr2RF+QqMMXT2dQ5YLWRnaCdgTSGp8FakAxZV/qoDTmpV/hgucBFLxmjqbmJX1y4SJpGucQFW1kWlr5KqQFV6GdRCVyFBV1CDFxmGC1AcMVEHNcYsGOL2fVjFMPuvnzRRfVCTV6g3xvYWq57Em/u6iCcNdhsUeV1TusglWANXa3cfdY0h6hrDdPbEsIswp9THiQvKqC3z43Lkf1AiGk/QEYmRSFpD9qxiL6csKmd++bSbtnEoxjImZ4vwHPBtnzHmPuA+ETkZqx5F/1S9E4wx+0SkAiuwvNEY80SW7XNTC8juAl+pdcnGGIh178+66O2CaEYwI9QAsSzJfK5AlloYhfuvOzyahaHU9DJhn43V5CciFLmLKHIXsaTEmgHZE+8ZMC3k9ZbXebX5VQCK3EUDpoUUugr1C5c8lzlVpICCAfclTZJoIsrO0E42tW8iSRIbtnTwoshdRIWvggpvBRX+CgpcBdbFXYDb7s52uLw13DKjOw9nR9T0ZYyhsSvK5qYQr+zqYFebdaLgdtgo9bvy4mS1PbI/KNHW3YcIzCr2sWZuMQvKA7in8PKXI5FMGrp6Y3T3Wd9i+9121swt4YjqAuaU+vC7NS3yYMY4Ju8BZmVcnwnsG+ZYT4jIfBEpM8a0pILHpKbc3Yc1ZeSAAMWkJWIFG1wBYKgsjFgq6yJ04FSScCO0bD4wC8PmzJ6B0R/I0CwMpfKKfjZWg3kdXmoLa6kttFagjSfjNEWarIBFpIHtndvZ2LYx3bbKX0WVzwpalHnL9FvzacQmtv1ZF4PiDUmTpC/Rx57QHrZ2bE1nXvTf53V4KfWWUuYto8JXQYmnJF1DJegM5t2qM3pWoHIiGk+wqzXCxoZQeuoGQNDtoKbQkxcR5q7eGJsbw9Q1hmgKRQGYUeTl1MXlLKgI4HPl93+/3lgqS8IYRGBemZ8VM4uYVx6gIuie0jVDpqAXgIUiUgvsBS4GPpjZQEQWAFtTRTKPBlxAq4j4AZsxJpT6/UzgxsPb/cPA7hxBFkZkYAZGbyiVidEF4SYrS2OwwVkYgwMZDq9mYSilVJ5w2BzpwolgfQnXHm1PTwlp6LaCFgAOcVDhGzgtZLp9U64sNrHhcXiyFtg0xhBPxumKdtEcaea1ptcAK1vDYDDG4HP6KPWUUuotpdxbTpGnKB288Dv9eB1Ta1p8fp8hqUmlvbuPbc1hXtvTycaGLowBm00o8jqn/NSNft3ROJubrKBEfWcvAJUFbk5aWMbCigBBT35FODOlsySiCRAo8Dg4fn5pOkvCk+dZIpOZMSYuIlcDf8VaZvROY8wbInJV6v7bgAuBS0UkBvQA708FKyqxpn2A9Z7xS2PMQzl5ILkkAi6/dQlWZ2+TjA/MwEhPKem0AhitW6w2mWyOLAGMwozbglYbpZRSU46IUOIpocRTwpGlRwLQHeseUMfilaZX0mn+JZ6SAdNCgs7glDqxVONPRHDanTjtTgIEDri/P4DRGe2kKdLEa8nXMMZgEysDPWmSOG1OCt2F1mvRW0Kpp5QCdwEBZyC95KrHPnm+IB7Rpx4R8QKzjTGbJrg/Ko/EE0n2tPdQ1xjild0dNIeiYAxel4OKoCdvVqXojSXY0hRmU2OIve09GKDU7+L4+aUsqghQ5HPluosTJhpL0NFj1ZIglSWxalYx88r9lAfdk2agyzejGZONMQ8CDw667baM378GfC3LdtuAFaPv7TRic4C32LpkYwzEeoZekSS8NXsWhtM/aPpI5jSSAnBqFoZSh5t+Nlaj5Xf6mV80n/lF8wGIJWI0RhppiDTQEG5gc/tm3mx9M922f0pIlb+KUm9p+sRTKTh4AAOsFWn6kn3sDe9le+d2YskYIpIu4GmMwWFzUOgupMhdlA6qFbgL8DusAIbP6cPv9B+W1WoOegQROQf4Bla6b62IrARuNMacO8F9U1NQZyTGjtZuXt/TyVv1XcQSSQAKvc68mboB1hSVbc3d1DWG2NUWIWmsx7hmbgmLKgOUBvIzRS9pDOHeOOFoHINVS+LY2hKOqinULInDRMfkKUwEXD7rEqzK3mZAFkYoI5gRgu5maNs6dBbGgEyM4MBMDM3CUGrcjGUcFpGzgFuxstl+bIy5edD9HwKuSV0NAx8zxrw2km3V1OS0O5kZnMnM4EyotL7xbuttS2dY1HfXs7Vzq9XW5rRWgUhlWVT4KnDZ8/eLMDU+7DY7XtuBq45k6g9iNHY3sju0m1gihsHKxOhfQtVg8Dq8FLgKKPIUUeiysjIWlyym3Fc+bv0dySeW67EKoj0OYIx5VUTmjlsP1JQWTyTZ22FlSby2u4OGziiIwe2wU+xz5kWBy37xRJLtLd3UNYbZ3tpNImkIuB2smlXMospA3mYMxBJJOiIx+uJJEJhd4uMdSypYUBmgMujRWhKH3/XomJy/RpKFEe8dNI2kf0WSLmjbBn3hA7dz+oZYUjW1KonTp1kYSo3c9YxiHBYRO/B94Ays4sUviMgDxpg3M5ptB04xxrSLyLuwVkY6boTbqjxgExtl3jLKvGUsLVsKQKgvNKCOxYuNLwIgCKXe0nSGRZW/ioAz+7foSg1nJEEMYwxxEyeaiLIntIftye209bZxwYILDnuAIm6M6czHEy916IwxdKSyJNbv7WRjfYi+RBIBCrxOaoryJ0sCIJE07GyzghLbmsPEEgafy87SmgIWVQapzqOskH7GGHpiCTp74hhjcNptLJ1RwPKZRcwp9eV1HY0pQsfk6UzEmtLh9B48CyNz+kj/75FWaNsOydig/dqHCGBk/J5nVcKVGoPRjsPHAltSU+cQkV8D5wHpIIMx5umM9s9irbo0om1V/upfsWFh8UIAookojd2NVtAiUs9bbW+xvmV9um21v5oqnxWwKPGU5N1nVZUbIoJTnDht+z8PxAdndY6DkQQoNojIBwG7iCwEPgk8fZBtVB6JxhPsae9hc2OIV3d30hK2VqTwOGx5lyUB1jSG/toZW5rCRONJ3A4biyqDLK4MMqPYiy3PBvr+ApfhvrgVjfe7OO2IChZXBplZ7M27v/EUp2OyGt5IszAGFPLMKOjZvgP6Qgdu5/QNLN45uKCn069ZGGq6GO04PAPYnXF9D3DcMO0/CvzlULcVkSuBKwFmz549gm6pqcZtdzO7YDazC6y/b8IkaO1pTU8J2R3aTV17HQAuu2tAHYsKX8VhqSOg1GiN5NX5CeA6IAr8EqsK/FcmslMqt4wxNIeibGu2siQ2N4VIGrAJFHjyq5ZEP2MM+zp72dwYYnNTmEhfAqddmFceYHFlkNklvrwp6tkvPXUjYa21PK/cKnC5oDJAqd+Vd3/jPKJjshqbzCyMQGX2NsmEFaQ4IIDRBZE2K4iR6Bu0X3uq9sUwK5LoXGmVH0Y7Dmd7YzVZG4qcihWgOPFQtzXG3I41NYTVq1dnbaPyi13sVPgqqPBVsKJ8BcYYuvq60lNCGrob2BXaBeyfQlLtr04HLYZL61fqcDtogMIYE8EahK+b+O6oXOmOxtnZGmFjQxfr93SmiiAafHm24kYmYwxNoSh1jSHqGsOEo3HsNqG2zM+iigC1Zf68yxzoiSXoiPRhDDjtNpbNKGTZzELmlfvxuTSaPhXomKwOC5sdPEXWJRtjIB4dWMQzc3WS9p2pWhiDzo0c3gNXJMn83RXQLAw16Y1hHN4DzMq4PhPYN7iRiCwHfgy8yxjTeijbKgVWKn6hu5BCdyFLSpYA0Bvv3b+8aaSe9S3rea35NQAK3YVU+6xgRXWgmkJXoX5RpXJmJKt4PAxcZIzpSF0vBn5tjHnnBPdNTaBYIsm+jh62NoV5bU8nezt6wBgcdhtFXicF3vyNpLaGo9Q1WsuCdvbEsAnMKfVzwvxS5pUHcDnyJyhhjCEcjRPqjWEQCjwOTllUzhHVBcwu8eVdAGY60DFZTQoi4PRYl0BF9jbJhBWkGFAHIxXM6GmHjp1ZsjBsGdNHMpZSzQxqaBaGyrExjMMvAAtFpBbYC1wMfHDQvmcDvwcuMcbUHcq2Sg3H4/Awt3AucwvnAtaqDU09Temgxfau7Wxs35hu21/DotpfTbm3HLtNV2pTh8dIvjIt6x+AAVJVhYf4NKImK2MMzeEoO1u6Wb+3i7rGEImk9c1W0OPIy2kbmToifdQ1hqlrDNHa3YcAM0u8rJ5TzIKKQF4tj5lMGjp7Y0T6EmAM1UVeTllczqLKIFUF+f13niZ0TFZTg81uTfHwFA7dJt57YCHP/p8duyH6JgdmYXgGBS0KM5ZW7c/C0OCrmlCjGoeNMXERuRprSogduNMY84aIXJW6/zbgy0Ap8IPU+3XcGLN6qG3H+4Gp6cNus6eneUCqEH60Y8C0kB1dO6y2qSkk/QGLKl8Vboc7h71X+WwkAYqkiMw2xuwCEJE5DDHnTU0uXb0xdrVG2FjfxRv7ughFrSqrXqed0oALhy2/P8CFemNsbrKCEo1dVmHPmkIPb19UzoKKAH53/kxpiCetehLRWBIRmF8RYPWcYuaXByj267eNeUbHZJU/HB4IDJOFYZKp6SOhgRkYvZ3WpXO3NdUkU2YWxlBTSfSDtRqbUY/DxpgHgQcH3XZbxu+XA5ePdFulxouIUOwppthTzJGlRwIQiUVoiDSklzh9rek1XuEVAIo9xfvrWPiqCLqC+iWYGhcjOUO7DnhSRP6Run4yqcrAanLpjSXY0x5hS1OY9Xs6aQz1ghGcdqEwz6dt9Iv0xdNBiX0dvQBUBN2cuKCMhZUBCvJoicxYIklbdx/xpMFuE46qKeDo2cXUlvnzKviiDqBjspo+xDaCLIxo9iVVo53QtQeaQ1agI5PDPUQhz/6LZmGoYek4rKYFn9PHvMJ5zCucB0AsGaMp0pQOWGxp38KbrdZKt36Hnyp/VfpS5i3DpuOoGoWRFMl8SESOBtZiVRD+D2NMy4T3TB1UPJGkvrOX7S1h1u/pYkdrN2BNDbZW2/BOi0hmbyzB1uYwdY1hdrdFMECJ38Xx80pZWBmg2Jc/GQTReIL2SB+JpMHtsLN6TjHLZhYxt8yH25E/01TU0HRMVmoQhxsc5eAvz36/SQ6shTE4kNG158AsDCRjRZL+AEZwYFBDszCmLR2H1XTltDmZEZjBjMAMAJImSXtve3p504buBrZ2bgXAYXNQ6atMTwup9FXi0hpCagSGDFCIyBJjzMbUAAz7KwXPTqW1vTzag4rITcB5QBJoAtYZY7JVMT4LuBVrrt2PjTE3j/aY+SCZtFad2NnazRv7BtaR8LsdVBV4sOXhahvZ9MWTbGuxghI7W7tJGij0Olk9t5hFlUHKAvnzwdFaeSNG0hj8LjsnLChnaY0WuZxuJnJMViqviW1/YGEo8ej+qSODVyTp2mPdNjgLw+7OPn0kfVtQszDyjI7DSg1kExul3lJKvaUsLVsKQLgvnM6waOhu4OXGlzEYBKHUW0qVryq9vGnAFcjxI1CT0XAZFJ/GSlf7Zpb7DPCOMRz368aYLwGIyCexCgJdldlAROzA94EzsJZWekFEHjDGvDmG404pxhjauvvY3d7Dm/s6eau+i55YEjB4nQ5K/a5pdYIaTyTZ0RqhrjHE9pZu4klDwO1g5awiFlUGqQi68yZjJNIXpyMSwwAFHgenLi7nqJpCZhZ7p00QSh1gIsdkpaY3h9u6+Muy32+S0NedfUWSaCd07bUKfg6QysJIr0qSbUUSty6rOrXoOKzUQQRcARa4FrCgeAEAfYk+GiON6YDFxvaNbGjdYLV1BtLBimp/NcWeYp0WooYOUBhjrkz9PHW8D2qM6cq46id7YaFjgS3GmG0AIvJrrKyLvA5QdEZi7G6PsKkhxBv7OunqjQMGl91OoddJiX96/adNJA2726ygxNbmbvoSSbxOO0dWF7CoKpg3q48YY4j0JejssYISJT4XZx5VyZE1hVRPo8wYNbSJHJOVUgeRLrwZBGZkb5PoG3pFklA9tNSBSQzcxu4aupCnp9BakUSX9ps0dBxW6tC57C5mBWcxKzgLgIRJ0NrTml4pZG94L5s7NlttbS4q/ZXpoEWFrwKnLX/qx6mROWgNChG5NNvtxpifj+XAIvJV4FKgE8g20M8Admdc3wMcN8z+riRVoGj27Nlj6dphFY7G2d1mFbbcsLeT1u4+MOC0CwVeJzOK8r+w5WBJY9jb3kNdY4gtTWF640ncDhsLKgIsqgwwq9iXFyfsg4MSpX4XZy2t4siaAl0OVA1posZkpdQY2V1WBsaQWRjGysIYkIGREcgI1UOsZ9BGYgUphpxKkqqFoe8Xh5WOw0qNXv+SpRW+CpaXL8cYQ6gvZGVYRKypIc83PA+ADRtlvrL0SiFV/ip8Tl+OH4GaaCMp9b8m43cPcBrwMjDsICwijwBVWe66zhjzB2PMdcB1InItcDXwX4N3kWXbIZdwMsbcDtwOsHr16km75F6kL86e9h62NIV5Y18njV29CIJNhAKvI28yAg6VMYaGrl7qGsLUNYWI9CVw2oXaMj+LK4PMLvXlxbKo/UGJrp4YSaygxLuXVXNEdQGVBfkzRUVNqFGNyUqpHBOxVgdxB4Ca7G0SsaFXJAk3ZM/CsDkHZl0csCJJULMwxp+Ow0qNExGhwF1AgbuAxSWLAeiN9w6YFrKhZQOvmdcAKHQVplcKqfZXU+Qu0s/PeWYkq3h8IvO6iBQCvxjBdqePsA+/BP7MgQGKPcCsjOsz2V+MaMro6UuwtyPC1uZuNuztpL7TmqNqm2YrbWRjjKE5HKWu0VoWNNQbx24T5pb6WFwZZG6ZH2ee1NjIrCmxP1OiUIMS6pCNdkxWSk0Bdif4Sq1LNsZALLI/aDE4mNHSaN0/mCswRCHP/hVJPJqFcQh0HFZqYnkcHuYUzGFOwRwAEskEzT3N6eKbO7t2sql9k9XW7hkQsCj3lmPXoOyUNpIMisEiwMKxHFREFhpjNqeungtszNLsBWChiNQCe4GLgQ+O5biHQ28swZ72Hra3hHljXxd72610TREIup3TNkMiU1t3H5saQ2xuDNEeiWETmF3i4/h5pcwr9+fNcpnW6ht9JA0U+5y8c2klS2uKNCihxtuYx2Sl1BQhAi6/daE6e5tE7MCVSPqDGOFGaN0CyfjAbTKzMLLWxAiCbTQfGacNHYeVmkB2mz0dhFjJSowxdEQ70nUs6iP17OjaYbUVO+W+cqp91eltPA5Pbh+AOiQjqUHxR/ZPrbABRwK/GeNxbxaRxVjLjO4ktYKHiNRgLSf6bmNMXESuBv6KtczoncaYN8Z43HE3dEBCCLjtVBV6sOnJKJ09MeoaQ9Q1hmgJ9wEws9jLqtnFLKgI4HXmR1AiGkvQlloStMDj4PQjKjlqRqEGptS4maAxWSmVL+xO8JVYl2wGZGFkWZEk3ASx7gO3c/mtmhf9AYvMOhjuAnB6p00Who7DSuWWiFDsKabYU8wRpUcAEIlF0tNC6rvrea3lNV5pfgWAYnfxgCyLAleBfi6fxEYSDv9Gxu9xYKcxZs9YDmqMuXCI2/cB7864/iDw4FiONd4yAxJv7utiT/v+glZBj0MDEhnCvXE2N4WoawzT0GVNbaku9HDKonIWVgTwu/Pj25i+eJK2SB+JpMHvsnPKojKWzyxiRpEuCaomxLiPyUqpaWQkWRjJePYVSaJdVgAjaxaGY/gVSfIrC0PHYaUmGZ/TR21hLbWFtQDEk3GaIk3paSFbO7fyVttbVluHb0DAotRbil3y48vSfDCSGhT/OBwdmaz6a0hsa+7mjX1d7OvQDInh9PQl0kGJvannqjzo5oQFpSyqCFLgzY+lguIJKygRSyRxO+wcV1vCyllFzCn1Y9eghJpAox2TReQs4FasjLQfG2NuHnT/ecBNWJltceBTxpgnR7KtUirP2BwjyMLoyZKBkfq9exv0hQ/czukfZkWSAnD6pkQWxnT/bKzUVOCwOagJ1FATsIoSG2No621LBywaIg1s69yWblvhq0hPC6n0V+K2u3PZ/WltyACFiIQYetWMKLAVa0WORyeiY7nSHY2zt6OHbc3WlI36zt70e2XQrRkS2URjCbY2d1PXGGJXewSTqrmwtraERZVBiv2uXHdxXCSThvZIH72xBHa7sHJmMcfMLaY2j4p5qslrLGOyiNiB7wNnYBUgfkFEHjDGvJnR7FHgAWOMEZHlWOnKS0a4rVJqOhEBl8+6BLMt2IaVYZEOWgzKxoi0QNs2SMYGbmNzDJo+kmV1khxmYUyXz8ZF7iLqw/WICIKQJInBgLHm9ztsjgMu+u2zmuxEhFJvKaXeUo4qOwqAcCy8v45Fdz0vN71svdaBUk9pOsOiyl9F0BXMZfenlSFHeWPMkH+F1AfWpcDdqZ9T3sNvNvDq7g4aO6Mg1iobWtRyaLFEkm2poMTO1giJVM2FY2YXs6gySFnAlRfPmzGGrt444WgMQVhSVcBx80pYUBHAkyd1M9TUMMYx+VhgizFmW6r9r4HzgHSQwRiT+XWnn/0fwg+6rVJKHcDmAG+xdcnGGIj37i/kmRnI6O2Ctu1DZGH49gcvDnPhu+nw2VhE+NQxn6In3kM0HqU33ktvopeeeA+98V66+roI9YWsSyxEuC9MR28HCZNAkAGf/ZImiYjgtDlx2BwDftpEv9hRuRdwBlhQtIAFRQsA6Ev00RRpSi9vWtdexxutb6TbZgYsSjwl+jqeIKMKQxtjEsBrIvK/49yfnHloQwPFPhc1RRqQGEo8mWRna4S6xhDbmruJp2ouLJtZyOLKYN6sTmGMIdKXoKPH+mZnTomPs5dVs6Q6SNCTH1NUVH4ZwZg8A9idcX0PcNzgRiJyPvA/QAVw9qFsm9r+SuBKgNmzZx/CI1BKTTsiVmFNp3eYLIzE/uKd0dD+5VV7QxBpswqCThL59NnYaXPidDlhhAmwxhhiyRg98Z70pTduBTVCfSE6+zrpinalgxrtve3p4IVgfW40GIwx6QCG0+bEaXdqdoY6rFx2FzODM5kZnAlYQbbWntZ0hkV9uJ4tHVustjYXlb5KqgJVVPmqqPRV4pxEY9JUNqY8OWPMj8arI5OBz2XPixPs8ZRIGva0R9jUGGJrczd98SQep40jqgtYVBmgpsibN1NeovEEbd3WChylfhfnrKhmaU0hpQGdg6amhmHG5Gz/SQ9IUzbG3AfcJyInY9WjOH2k26a2vx24HWD16tVDpUErpdTI2OzgLbIu2XTuzn57DuXbZ+OREBFcdhcuu4tCd+FB2xtj6Ev2EYlF6In3pH+GY2E6o5109nXS2dtJV18XHb0dJEkOCGT0c9lc6UCG0+bELvo5Xo0vm9go95VT7itnWfkyjDGEYqH9dSy6G3ih4QUABKHMW5bOsKjyV+F3+nP8CKamvCmnrMaPMYa9HT3UNYbZ0hSmJ5bAZbcxv8LP4sogM4t9eVMIMp5M0tbdRyxh8LnsvH1ROStmF+nUHpVv9gCzMq7PBPYN1dgY84SIzBeRskPdVimllBqOiOC2u3Hb3RQzxBSgFGMM0USUSDxCJBahO9ZNT7yHrmgXbb1tdEQ70pe+RF865b4/I8MmNit4YnPhtDtx2fJjCrLKDRGhwFVAgauARcWLAIgmouk6Fg3dDbzZ+iavt7wOQIGrYMC0kGJ3sb7+RmBEAQoRmQMsNMY8IiJewGGMCU1s19ThZIyhsStKXWOIuqYQ3dEEDpswr8zPoqogc0p8OPKkEKQxho6eGN3RBE67sGJmEWtqi5lb6s+bx6jy2yjG5BeAhSJSC+wFLgY+OGifC4CtqSKZR2Ml97YCHQfbVimlphv9bHx4iAgehwePw0OJZ4hVZVL6En10x7oHXNp722nrbaO9t532qPU7hvRJYtIkBwQxXHYrK0NPItVIue1u5hTMYU7BHAASyQQtPS00RBqoD9ezK7SLuva6dNsqvzUlpNpfTbmvHEf+LL88bg76jIjIFVhzikuA+Vjfnt0GnDaxXVMTzRhDS7jPCko0hujqjWMXYU6pj0ULgswrz6/VKbqj8XRdifnlfo6fV8riqgK8Lp3bqKaO0YzJxpi4iFwN/BVrqdA7jTFviMhVqftvAy4ELhWRGNADvN8YY4Cs207YA1RKqUluLJ+NR7Dk8xLgp8DRWCuCfCPjvh1ACEgAcWPM6vF4PPmif5pJsWforIykSaaDF+G+MOFYmI5oB82RZiuQEW2npaclXfDTYEiaZDp44ba7cdldWhxRDclus1Ppr6TSX8mK8hUYY+js6xwwLWRn107AmkJS4a1ITwmp8lfhdXhz/AhybyQhm3/HquL+HIAxZrOIVExor9SEao/0UdcQoq4xTFukDxGYVezjuNpS5pf7cefR6hR9cWsKR8IkKfW7OXdFDctmFObN0qdqWhrVmGyMeRB4cNBtt2X8/jXgayPdVimlprFRjcMjXLa5Dfgk8N4hdnOqMaZlDH2f1mxiI+gKWktGDlEeIJ6M0x3rTq9W0tXXRXNPM609rbT2tNIUacIYkw5gGGNw2pzWtBWHW6eRqAFEhCJ3EUXuIpaULAGgJ94zYHnT11te59XmVwFrmd/MOhaFrsJp93oaSYAiaozp639iRMTB0GtAq0mqqydGXZMVlGgORQGYUeTl1FnlLKgI4HPlT3pRMmloj/TRG0/gdth524JSjp5dzMxi77T7D67yko7JSimVW6Mdh0ey5HMT0CQiZ2ffhZpoDpuDQnfhkAU/+7Mw+gMYHdEOmnuaaY4009LTQmtPa3qFkv4pJP01N9wOt65KovA6vNQW1lJbWAtYQbGmSFM6aLGtcxtvtb2VbpuuY+Grosxbht2W36+hkZyV/kNEvgB4ReQM4OPAHye2W2o8dEfjbG4KU9cYor6zF4CqAg8nLSxjUUWQgCd/ghLGGLr7EnT2xBAMS6oKOX5+KQsqArgcmoan8oqOyUoplVujHYdHvGzzEAzwNxExwI9SKycdQJd8nlgDsjCyiCfj6cyLzmgnbb1tNHQ30BRpoqWnhWQymc6+wIDH4cFtd+NxeHTqyDTlsDmoCdRQE6gBrPOa9mj7gGkh2zu3W23FQYVv4LQQtz2/VhwcyRnq54GPAuuBf8NK8/3xRHZKjV5PLMHWpjCbGkPsae8BoCzg4m3zS1lUGaTQm1/r86ancCQN5UE356+qYemMorx7nEpl0DFZKaVya7Tj8IiXbR7CCcaYfanpJA+LyEZjzBMH7FCXfM4ph81Bsac4ay2M/uyLjmgHXdEuWnpaqO+upynSlJ46gljtHDYHXrsXj8OjhTunGRGhxFNCiaeEI0uPBKA71j1gWsgrTa+kl90t9ZSmgxXV/moCzsCUfr0cNEBhjEkCd6QuahKKxhNsa+6mrjHErrYISQNFPifH1pawuDJISZ7VW0gaQ0ckRk9fApfTplM41LSiY7JSSuXWGMbhMS3bbIzZl/rZJCL3YU0ZOSBAoSavAdkXgxIwkiZJqC9Ee287HdEOGrsbrW/PIw1W4U6RdDjL4/DgdXhx29362Xea8Dv9zC+az/yi+QDEEjEaI43poEVdex1vtL6Rbtu/UkiVv4pSb+mUys4ZySoe6zkwutsJvAh8xRjTOhEdU8OLJZLsaOlmU2OIHa0REklD0ONg1exiFlUEKA/m34AV6YvTHomBgUWVAd62oIyFlQHcjvyeh6VUJh2TlVIqt8YwDh90yedhjukHbMaYUOr3M4EbR9N/NTnZxDZk7YveeC8d0Q7ae61VRvaG97IvvI/6SD02bOlinV6HVwMX04TT7mRmcCYzgzMBK8DVP52ovrue+u56tnZutdranFT6KtMZFpW+Spz2yZttPpIpHn/BWs7ol6nrF6d+dgF3AeeMf7dUNvFkkl2tEeoaw2xrCRNLGHwuO8tqCllYGaC60JN3g1E8maQ13EcsmaTI6+TsZdWsnFWkq3Co6UzHZKWUyq1RjcMjWfJZRKqwAh0FQFJEPgUcCZQB96U+5zmAXxpjHhr/h6YmI4/DQ5XDSuHPFEvEaI+2097bTnOkmT3hPewN76W+ux6b2NKrjXgdXnwO36Q+KVVjYxMbZd4yyrxlLC1bCkCoL5SuYdHQ3cCLjS8CIAhl3rIBdSwCzkAuuz/ASAIUJxhjTsi4vl5EnjLGnCAiH56ojilLMmnY3W4FJbY2h4nGk3gcNhZXBllUGWRGsRdbngUljDGEonFCPXFsNuHo2UUcW1vC3FI/Nlt+PValRkHHZKWUyq1Rj8MjWPK5AWvqx2BdwIox9FnlIafdSYWvggpfBYtLFqdv70v00dbbRmtvK43djezq2sXe8F56enqwiY2kSeK2u/E6vFqcM4/1TydaVLwIgGgiSmO3NS2kPlLPW61vsb5lfbpt/0ohVf4qSjwlOfvieyQBioCIHGeMeQ5ARI4F+kMs8dEcVERuwlpWKQk0Aev659UNancn8B6gyRizdDTHmoqMMezr6KWuMcTmpjA9sQQuu4355X4WVQaZVeLDnocn6n3xJK3dfSSSSWqKvLx7aTVH1hTgd+fPaiNKjYNxH5OVUkodEh2H1aTmsrvS34wfVXoUkFrxLtZNS08LLT0t7A7tZlfXLhojjeklUR02Bz6HD6/Dm/dLWU5Hbrub2QWzmV1gre6TMAlae1rTU0J2h3ZT114HpF5DGXUsKnwVOGyH55xsJEe5HLhTRAJY1Ye7gMtT89/+Z5TH/box5ksAIvJJ4MvAVVna3QV8D/j5KI8zZRhjaApFqWsMUdcYJhyN47AJtWVWUGJuqQ+HPf+im8YYOnpidEfjuB12TlxYxjFziqnJw+kqSo2TiRiTlVJKjZyOw2rKERECrgABV4C5hXNZXbUasKaJtPa2WrUtQnvZ0bWDfeF9JElijMEmNvxOPz6HT4MWecYu9nQGzoryFRhj6OrrGjAtZFdoF2BNISn3lqfrWFT5q/A6vBPSr5Gs4vECsExECgExxnRk3P2b0RzUGNOVcdXPEEssGWOeEJG5oznGVNES3h+U6OyJYROYU+rnxAVl1Jb5cTnyLygB1sojbeE+ksZQWx7gpIVlLK4KasFLpQ5iIsZkpZRSI6fjsMonTrsznW3RX7sgkUzQ1ttGc08ze8N72d6xnb3de0kaK2hht9nTQQudHpI/RCRdqHVJyRLAKtDaX3izobuB9S3rea35NQAK3YUUuYpYUrxkXPsxklU83MCFwFzA0f+ttjFmTJWDReSrwKVYVY9PHcu+Uvu7ErgSYPbs2WPd3YTqiPRR1ximrjFEa3cfAsws8bJmbjHzywN4nPl5kr5/edA4Hped046sZNXsIiqCnlx3TakpY6LGZKWUUiOj47DKd3abnXJfOeW+co4sPRLm7A9aNEWa2B3azdbOrTR0NyAICZPAY/fgd/p1BZE843F4mFs4l7mFcwGIJ+M09zSngxb7wvvoifeM6zFHMsXjD1hBhJeA6Eh3LCKPAFVZ7rrOGPMHY8x1wHUici1wNfBfI913NsaY24HbAVavXp01IyOXunpjbE4FJZpC1tNYU+Th7YvKWVARyOs6C72xBG3dfRhgUUWAExeWs7AygDMPp6wodRiMakxWSik1bnQcVtNOZtDiqDKrrkUsEaO5p5nG7kZ2dO1ga8dWGiINABgMAUcAv9OvU0PyiMPmoNpfTbW/mlWsoj5cz/yi+eN7jBG0mWmMOetQd2yMOX2ETX8J/JkxBigmo+5onC1NYTY1hqjv7AWgssDNSQvKWFgZIOjJ36V+ksbQ3t1HTyxBwO3gzKMqOXp2MaUBd667ptRUN6oxWSml1LjRcVgprOkhNYEaagI1rKpcBUB3rJvG7kb2hveyuWMzu7t2E0/GMRhcdhdBZxCX3aVZFnlCRMZ9ms9IAhRPi8gyY8z68TqoiCw0xmxOXT0X2Dhe+8613liCLc1WpsSeth4MUBpwcfy8UhZVBijyuXLdxQllZUtEMQYWVwc5cUE5CysCeVngU6kcGfcxWSml1CHRcVipIfidfuYVzWNe0TxOmnkSiWSClp4WGiINbOvYxpaOLbR2tyIIIkLQFcTn8GnAQqWNJEBxIrBORLZjpbEJYIwxy8dw3JtFZDHWMqM7Sa3gISI1wI+NMe9OXf8V8HagTET2AP9ljPnJGI47IfriSba1hKlrDLOztZukgUKvkzVzS1hUGcj7rIH+2hKRvjgBt4N3Lq3i6NkllPjzOxijVI5MxJislFJq5HQcVmqE7DY7lf5KKv2V6ZUiQrEQ9eF6dnbtpK69jobuBut/ERBwWtNCtPjm9DWSAMW7xvugxpgLh7h9H/DujOsfGO9jj5d4Isn21m7qGsNsb+kmkTQE3A5WzipiUWWQimD+F4iJxq3aEkkDiyoDnLRQsyWUOgzGfUxWSil1SHQcVmqURIQCVwEFJQUsLlnMmXPPpCfeQ0N3A7u7drOpbRO7w7sxxlh1LJwB/C4/dtE6FtPFSJYZ3QkgIhXAtF5uIZE07GqLUNcYYmtzmFjC4HXaWVpTwMLKIDWFnrwPShhj6OiJEelL4HHaOO2ISo6ZU0xZnmeJKDVZ6JislFK5peOwUuPL6/BSW1hLbWEtJ886mVgiRkN3A7tCu9jYtpFdXbtImiRgTSHRgEV+G8kyo+cC3wRqgCZgDvAWcNTEdm1ySBrD3vYeNjWG2NIUJhpP4nbYWFQZZFFlkJlFXmy2/A5KgDWNpbU7StJAbamfUxaXs7gqqCtxKHWYTfcxOS8ZAxgwSet3k0xdH/T7gJ+Q+id12wikA+iSSqUVEFvqZ+rSf5v0/8y4XykF6Dis1ERz2p3MKpjFrIJZnDDjhHTAYmdoJxtbN7IrtAuTeg8MOoP4nD6dEpJHRjLF4yZgLfCIMWaViJwKTNqpF+PBGEN9Zy91jSE2N4WJ9CVw2oV55QEWVQaYU+LHPg2CEsYYQr1xunrjuB02Tl5Uzpq5JVQW6JcFSuXQtBuTJwVjIBkHk4Bk6mKy/DQmdfKf+qAkWHEEkdRPk44r7A84ADY72BzWxe4AcVi32d3WT3GALRUwELv1O2L9nhlcyN75gcGP9CUByWTGY4pbl0Q09TOWui2xP6CRGdzoD5L0H9b0Px5JPZ7+x5Tx2GyO/X1WaurScVipwygzYHHijBPpS/RR313Pzs6dvNH2BvtC+6y3HmwEXUG8Dm/eZ7Xns5EEKGLGmFYRsYmIzRjzdxH52oT37DAzxtAU6mVzYzd1TSFCvXHsNqG21M+iygC1Zf5pU1shnkjS0t1HIpmkpsjHOStqOLKmAI9TU6mUmgSmxZg87oyxTsQTcUjG9p+M91+MycgWSG9knYSbpHVC7fCA0wMO7/9n777j5C6rxY9/zvS2vSTZbEIKCQmkEUIT6UVQOiogijS56MV6LSgWFPHHtaNyLxcR0HsRUBFFRa+gcOklgQABAqRnS7LZ3Wzf2Snf5/fH9zuzs5vdzWyf2T3vF8PMfNs8M5M9850zz3Me8AbBG3Ku+912+5xEg7fvbZe3NwHh6nfJ5RMpYzKSFfHe1zAZh2Ss33UPxKMQ64RYR+91vNO+HW2FRI+TaAFM6vU2drJEXBmvm9e57XXeG6VyhsZhpSaRz+3jgMIDOKDwAI6bcxxd8S7qOurY3LKZ15tfp76zPr1doa8Qn1sL9+eTbBIULSISAZ4A7hGRBiAxvs2aWP/x+Cb+/kYDHT0JXAJzS0O8a0EZ8yvC+D3T50t5Z0+Clq4YbreLw+eVcNSCMmYXawZSqRwz5WPyfhmz7xfl1C/+mN4hCvbGzi/9lp1Y8IUhUAT+CPgi4C+wL74IePzOJWBfu/29y9y+3E4ijCcR8PiAMTrBs5IQ74ZE1L6Od0O8y76OtkL3Xoi2OJdW6GzobQf0Jo1cHvt9Sb0/0/k9UhNN47BSOSTkDXFgyYEcWHIg75n/Hlp7Wqlpr2Fj80Y2Nm+kqbsJgyHsDVPgK9DhIDkumwTFOUA38FngEqAI+NZ4NmqivVnfTtDn5oj5JSyqLJhWPQUsy9DUGSOWsCiN+Hj/YXNYXl1E2J/NPw2l1CQYUUwWkdOBWwA39nTON/dbfwnwJeduB/BxY8wrzrptQDuQBBLGmDVj8kwGkuiBzsbeXg6Q8et5xpAIb8hOLIRL7YRDoAgCxXaiwRvo7dHgCfb2enDpCUlOcLntBJE/kt32xtgJjJ4Op1dGB/S0Q1czdOyBriboaoTO3dhDa5xxNcbqTWCkkk56UqrGxpQ/N1YqnxX5iyjyF3FI+SFYxqKhq4Htbdt5vel1trZuxRiDW9wU+gsJuKf+JAf5ZshvoSLiBv5ojDkFsIBfTkirJtiPL1zFF3/3yrTqLRCNJ2nuioGBZbOLePeicuaXhadFwU+l8tVIY7Kz363AqUAN8KKIPGSMeSNjs63A8caYvSJyBnA7cGTG+hONMY1j8TwGFamEmcsgWGInG4Kl9pdYbwh8IfCGe4dTaLJh+hCxe774wsCMwbdLJqCnzU5eRFvtS/su6NgNHQ3QXuckuFw43TDsXjLeoJ3AcHsn5vmovDZdzo2Vmipc4mJmeCYzwzM5ctaR9CR7qGmv4Z2977ChcYM9HEQg6A5S6CvE7Zo+P1TnqiETFMaYpIh0iUiRMaZ1oho10aZDwUuw62y0dsfp6EkQ9nt4zyEzOGxuKSVhHZelVD4YRUw+AthkjNkCICL3Yf8CmE5QGGOeydj+OaB6LNo8LJVL7YtSI+H2QKjUvgzEsuwERrQFulvsnhdtdfalY1dvbYzUTCmejJ442vNCOabLubFSU5Xf7Wdh8UIWFi/kPfPeQ3O0me1t29nQuIHNLZuxsHDhoshfRMCjEwNMhmz68UeB10TkEaAztdAY86lxa5UaU4mkRWNHjIRlMb88zAfWzNEpQpXKXyOJybOBnRn3a+jbO6K/K4G/Ztw3wN9FxAD/ZYy5faCdRORq4GqAuXPnDvUclJp4LhcEi+1LSb91xthFPLv3QrczdKR1B7TWQnt9b6FUK2n3uvCFnZ48+kvbNKXnxkpNASJCWbCMsmAZq2esJpaMUdNew1t73+K1Pa9R11kHBiLeCBFfRGtXTJBsEhR/cS4qz6SKXnrcLo5cUMpRC8qYVaTjrJTKcyOJyQP90ZsBluFMl3cl8O6MxccYY+pEpBJ4REQ2GmOe2OeAduLidoA1a9YMeHylcpJIb12M4jl911nJ3h4XnXugZYd9aavtnYLVsuweF76w9riYHkZ8bpxFPaAlwF3AauB6Y8z3s91XKTU6PrePBcULWFC8gNPnnU5jdyNbWrfw6p5X2dG+AwCvy0uRvwivS4cFjpdsEhT3Awdin8xuNsZEx7dJajQsY2jujBGNJykN+7jgsDms0KKXSk0lI4nJNUDmt65qoK7/RiKyArgDOMMY05Raboypc64bRORB7CEj+yQolJqSXG4Il9mXisXAMfZyy7KHi3Q2QvtuaNkGe7c5tS7A/p/YhVt9Ibtgp5oqRnRunGU9oGbgU8C5I9hXKTVGRISKUAUVoQqOnHUkXfGudKHNN5reIJ6MIyL2UBAttDmmBv3WKiIe4DvAFcB27KpS1SJyF3ZGNz4xTVTZiCUsmjp6sIClswo4fnEFC8ojWvRSqSlilDH5RWCRiMwHaoGLgA/1O/5c4PfAR4wxb2csDwMuY0y7c/s0tFq9UvaQkVTNi4rFwLH28mTCnlWkY7c9RKRpE7Rst6dRTfGG7MSFTo2aV8bg3DibekANQIOIvG+4+yqlxk/IG2Jp2VKWli0lYSWo66jjrb1v8UrDK9R31YOBAl8BEW9EkxWjNNTP6t8DCoD5xph2ABEpBL7vXD49/s1TQzHG0N6ToK07jt/r5oQllRwxv5TyiH+ym6aUGnsjjsnGmISIXAv8L3bX4DuNMa+LyDXO+tuArwNlwH84H6yp6URnAA86yzzAr40xfxufp6jUFOD2QMFM+zJrpb3MGLu3Rftue2hI4ybYuwU6G0hPi5pKWnj0MzyHjfbceLj1gEa0r9YDUmp8eVwe5hbOZW7hXE6ZewqN3Y1satnE+j3rqW2vBSDoCVLoL8QtWqtouIZKUJwJLDbGpMcRG2PaROTjwEY0QTFpkpahqbOHWMIws9DPmctnccjsIgJe/QNQagobVUw2xjwMPNxv2W0Zt68Crhpgvy3AytE1XalpTsSePjdYApVL4MCT7aRFT5udtGitgca37d4W6aQFdsLCHwGXDtPMEaM9N866HtBo9tV6QEpNnMyhIEdXHU1brI0tLVt4Zc8rbGrZhDEGv9tPkb8Ij8byrAz1KpnMAJyxMOlUclcTrCeRpKkzBgZWVBfx7kUVzCsLaTcipaYHjclKTSUiECiyLxWL4cCT7KRF915o32UX4tyzEZq3ghW317l94C+we1voZ/9kGG0czqoe0Djsq5SaIIW+QlZVrmJV5Sq6E91sb9vOq3te5Y2mN0hYCTwuDyX+ErxuLbI5mKESFG+IyKXGmF9lLhSRD2NnidUEMMbQHk3QFo0T9Lk57eAZrDmglJKwFttSaprRmKzUVCfSW9dixsFw0On2TCEdDXbxzcZ3oGEjtNXY2xvshIX2spgoo43D+60HNE77KqUmQdATZEnpEpaULiGejLOjfQcbmjbwasOrxJIxXOKiOFCM361D+zIN9Wn2r8DvReQKYB32x+DhQBA4bzQPKiI3Yhf2sYAG4LJUlfiMbeYAvwJmOtvdboy5ZTSPm0+SlqGpo4dY0qKqOMjZq6o4uKoQv0eHcSg1TY1bTFZK5TCXGwpn2ZfZh9nLYp3QVgd7t8Pu16F5M1gJu5eFNwj+Qq1lMT5GFYezqQckIjOBtUAhYInIZ4CDnaEk++w75s9QKTUuvG4vC4sXsrB4Ie+b/z5qO2p5o+kN1jespynahAsXxf5iAp7AZDd10g2aoDDG1AJHishJwCHYY9/+aoz5xxg87veMMV8DEJFPYRdnu6bfNgng34wxL4lIAbBORB6Z6tMp9Q7jMKycU8Kxi8qZW6rDOJSa7sY5Jiul8okvDOWL7MuiU+xeFu27oHUnNLwJDW9A525AwOW1h5F4AjosZJTGIg5nUQ9oF/bwjaz2VUrlH4/LwwGFB3BA4QG8Z957qO2oZWPzRl7a/RLNHc24xEWRv4igJzjZTZ0U++0PaIz5J/DPsXxQY0xbxt0wAxT5McbUA/XO7XYReRO7gvGUS1D0H8Zx6tIZHDG/lOKQDuNQSvU1HjFZKZXnXG4omm1f5h5l96ToanLqWLxl97JoqwEExGUnLLSOxYhpHFZKjRWXuJhTMIc5BXM4ee7J7OrcxZtNb/JSw0vUddQhIhT7i6dVsmLSBiyKyE3ApUArcOJ+tp0HHAo8P8Q2eTelkmUZmjpjRBNJZhXpMA6llFJKjQERCJfbl9mr7WXdLXbCovFt2PWa3dtCXJqwUEqpHOESF1WRKqoiVZw09yQ7WdH8Jut2r6Ouo86uWTENhoGMW4JCRB7Frh/R3/XGmD8aY64HrheRLwPXAt8Y5DgR4AHgM/16XvSRT1MqxRIWTR09WAZWzCniWJ2NQymllFLjKVhsX2atgOXvh2hrbw+LXa86CQsdEqKUUrlARJgVmcWsyCxOnHMi9Z31vNn0Jmt3r6W5oxkRodRfin8K1hsatwSFMeaULDf9NfAXBkhQiIgXOzlxjzHm92PYvEnREU3Q0h3D73Vz0tJKjphfRqnOxqGUUkqpiRYogpnL7cvy9zs9LLbbNSzqX3WGhBhwB5yExdQ7CVZKqXwgIumeFSfOtZMVrze+zksNL9HU2YRLXJT4S6bMbCCTMsRDRBYZY95x7p7NAFMzid2d4BfAm8aYH05k+8aSZQzNnTGi8SQVBX4uOnwuy6uLCHh1GIdSSimlckS6h8VKWHEhdDVDyzbYtcEeEtLZYG/nDUOgUKc1VUqpSeASF7Mjs5kdmc0pB5xCbXstrze9zrrd62jubsbtclPiL8Hr9k52U0dssj5dbhaRg7CnD92OM4OHiFQBdxhj3gscA3wEeE1E1jv7fcWpYJzz4kmLpo4YSctiyaxCTjioggXlEVwu7S6plFJKqRwmAuEy+zL7MLAs6NgNTZuhfj00vmXPHILYvSt8ER0OopRSE8wlLuYUzmFO4RxOPeBUdrbv5NXGV1nfsJ5YMobX5aUkUIInzxLKk9JaY8wFgyyvA97r3H4Ke/qmvNIVS7C3K47HJbzrwDKOXlBGZeHULmSilFJKqSnM5YLCWfZl/rshGe8tuFn3MrTWONt57V4YU7yAm1JK5Rq3y828onnMK5rHGfPPYHvrdtbvWc+Gxg3ErThBT5AifxFuyf1e/PmVTslRxhhauuN09iQoCno5d1UVh84tIezXl1cppZRSU4zbC2UL7ctBZ0C0DfZus4eC1K+Hzj2AAV+B3cNCXJPcYKWUmj68Li8HlhzIgSUHctbCs9jSsoV1u9fx1t63MMYQ9oYp9BXm7AQN+g16FJKWobGjh3jSYl5ZmAsPn8NBMwrwuPWDWCmllFLTRKDQnh1k1gpYeTF07LKHg9SshaZNgAGXGwIl4A1OdmuVUmra8Lv9LC1bytKypXTGO3ln7zu8uOtFtrdtB6DQX0jYE86pZIUmKEagJ5GkqTMGBlbPLeHdi8qpLgnm1BurlFJKKTXhXC4orLIv84+FeLfdu2L361C7DlobwRjwa+8KpZSaSGFvmFWVq1hVuYqWaAsbmzfywq4XqO+sRxBKAiUEcmCIniYohiE1TWjA6+a0g2dw+LxSikM6TahSSiml1IC8Qahcal+WXWAX22zcBHXrYM/bgAHxQKhEa1copdQEKQ4Uc1TVURw560gauhp4vel1Xtj1As0dzXhcnkmdCUQTFPthnGlCu51pQi8+Yi7LZus0oUoppZRSwyICBTPty/x3270rmrfCrlft3hWdjYAF/iLwF+rMIEopNc5EhBnhGcwIz+CEOSdQ017DK3te4eWGl4klY/jdfooDxRNaXFMTFINIJC0aO2IkjWHxjAgnHlTJwgqdJlQppZRSakx4gzDjYPuy/IPQXgd73rJrV7RsBwx4ghAsgTybJk8ppfKNS1zMLZzL3MK5nD7vdLa0bmHt7rVsbN6IMYaIN0KBr2DcyxpotO8nGrfrS7gFjlhQxjELy5lZpF0OlVJKKaXGjcsFRdX25cCTIdpqF9qsXWfPDmIl7HoVwVIttKmUUuPM6/ZyUOlBHFR6EJ3xTt5qfovn65+ntqMWEaHYX0zQMz6xWBMUGWpbugn7Pbxv+SzWzCuhIDA5426UUkoppaa1QBHMXm1fEjGn0OYGqHnBLrSJsYttKqWUGldhb5jVM1azesZqGrsbeb3RrldR11FHe6x9zB9PExSOIxeUsagywiFVRfg8WlFaKaWUUioneHxQsdi+HHIetNdDw0Zo2aZDP5RSagKVB8s5fs7xHFt9LDXtNazfs56KUMWYPoZGdccH18yZ7CYopZRSSqmhiPROY6qUUmpSZNarGPNjj/kRlVJK5RwROV1E3hKRTSJy3QDrLxGRV53LMyKyMtt9lVJKKaWUGguaoFBKqSlORNzArcAZwMHAxSJycL/NtgLHG2NWADcCtw9jX6WUUlnIIlksIvITZ/2rIrI6Y902EXlNRNaLyNqJbblSSk0MTVAopdTUdwSwyRizxRgTA+4DzsncwBjzjDFmr3P3OaA6232VUkrtX5YJ3zOARc7lauA/+60/0RizyhizZrzbq5RSk0ETFEopNfXNBnZm3K9xlg3mSuCvw91XRK4WkbUisnbPnj2jaK5SSk1J2SR8zwF+ZWzPAcUiMmuiG6qUUpNFExRKKTX1yQDLBpyfT0ROxE5QfGm4+xpjbjfGrDHGrKmoGNuKzkopNQVkk/AdahsD/F1E1onI1YM9iCaLlVL5bErO4rFu3bpGEdk+zN3KgcbxaM8w5UI7cqENkBvtyIU2QG60IxfaAPndjgPGoyFZqAEypyqqBur6byQiK4A7gDOMMU3D2bc/jcNjIhfakQttgNxoRy60AXKjHbnQBsivOAzZJXyH2uYYY0ydiFQCj4jIRmPME/tsbMzt9NYR2qOxOO/bALnRjlxoA+RGO3KhDZDf7Rg0Fk/JBIUxZtg/3YnI2lwYz5cL7ciFNuRKO3KhDbnSjlxog7ZjxF4EFonIfKAWuAj4UOYGIjIX+D3wEWPM28PZdyAah6dGO3KhDbnSjlxoQ660IxfakEvtGIZsEr6DbmOMSV03iMiD2ENG9klQZNJYnP9tyJV25EIbcqUdudCGqdwOHeKhlFJTnDEmAVwL/C/wJvAbY8zrInKNiFzjbPZ1oAz4j8wK8YPtO+FPQiml8l864SsiPuyE70P9tnkIuNSZzeMooNUYUy8iYREpABCRMHAasGEiG6+UUhNhSvagUEop1Zcx5mHg4X7Lbsu4fRVwVbb7KqWUGh5jTEJEUglfN3BnKlnsrL8NO9a+F9gEdAGXO7vPAB4UEbDP339tjPnbBD8FpZQad5qg6HX7ZDfAkQvtyIU2QG60IxfaALnRjlxoA2g7prJceU21Hb1yoQ2QG+3IhTZAbrQjF9oAudOOrGWRLDbAvw6w3xZg5bg30JYrr2sutCMX2gC50Y5caAPkRjtyoQ0wRdshdhxUSimllFJKKaWUmjxag0IppZRSSimllFKTThMUSimllFJKKaWUmnTTKkEhIh8QkddFxBKRQadCEZHTReQtEdkkItdlLC8VkUdE5B3numQEbdjvMUTkIKeKfurSJiKfcdbdICK1GeveO9w2DOe5iMg2EXkts6r/cPYfbRtEZI6IPCYibzrv3acz1o3qtRjsfc5YLyLyE2f9qyKyOtt9x7ANlziP/aqIPCMiKzPWDfjejFM7ThCR1ozX+uvZ7juGbfhCxuNvEJGkiJQ668bktRCRO0WkQUQGrIw+Ef8mpjrJgTic7XFknGNxts9lsH/fE/xajEsszuLvfkL+5rJox7jH4izaMO5xOMt2aCyeAiQHYnGWsUfPiXu30XNiPSdOrZ/acdgYM20uwFLgIOBxYM0g27iBzcACwAe8AhzsrPsucJ1z+zrg30fQhmEdw2nPLuAA5/4NwOfH4LXIqh3ANqB8tM9jpG0AZgGrndsFwNsZ78eIX4uh3ueMbd4L/BUQ4Cjg+Wz3HcM2vAsocW6fkWrDUO/NOLXjBODPI9l3rNrQb/uzgH+Ow2txHLAa2DDI+nH9NzEdLuRAHB7JcRiHWJxtGwb79z2RrwXjEIuzjD3j/jeXZTvGNRZn2YYTGMc4PJJjobE4by/kQCwe7jHQc2I9J9Zz4oG2n3JxeFr1oDDGvGmMeWs/mx0BbDLGbDHGxID7gHOcdecAv3Ru/xI4dwTNGO4xTgY2G2O2j+CxxrIdY71/VscwxtQbY15ybrcDbwKzR/BY/Q31Pme271fG9hxQLCKzstx3TNpgjHnGGLPXufscUD2Cxxl1O8Zp39Ec52Lg3hE8zpCMMU8AzUNsMt7/Jqa8HInDIznOeMTiXIjDWR1nnGJxLsThrNoxAbE4F+LwSI6lsThP5UgszoU4PJJ2jPX+WR1Dz4n1nHgQUy4OT6sERZZmAzsz7tfQ+8c/wxhTD3aQACpHcPzhHuMi9v1Hd63TlebOkXQjG2Y7DPB3EVknIlePYP+xaAMAIjIPOBR4PmPxSF+Lod7n/W2Tzb5j1YZMV2JnKlMGe2/Gqx1Hi8grIvJXETlkmPuOVRsQkRBwOvBAxuKxei32Z7z/TSjbeMfhkRxnPGJxLsThYR9nDGNxLsThbNuRaTxicS7E4WEdS2PxtKDnxH3pOfG+2+g5sZ4Tp4zZvwnPqJuWY0TkUWDmAKuuN8b8MZtDDLDMjFUbhnkcH3A28OWMxf8J3Oi06UbgB8AV49iOY4wxdSJSCTwiIhudjFpWxvC1iGD/8X3GGNPmLM76tRjokAMs6/8+D7bNqP+NDKMN9oYiJ2IH43dnLB7VezPMdryE3aWyQ+xxjX8AFmW571i1IeUs4GljTGZWd6xei/0Z738TU0IuxOH9tWOYxxlxLM6FODyG7RjrWJwLcTjbdtgbjl8szoU4nG07UjQW57hciMW5EIfHsB16TrzvNnpOrOfEmcbk38SUS1AYY04Z5SFqgDkZ96uBOuf2bhGZZYypd7qwNAy3DSKS1TEcZwAvGWN2Zxw7fVtEfg78ebCdx6Idxpg657pBRB7E7rbzBBP4WoiIFzsQ32OM+X3GsbN+LQYw1Pu8v218Wew7Vm1ARFYAdwBnGGOaUsuHeG/GvB0ZH4AYYx4Wkf8QkfJsn8NYtCHDPr+gjOFrMdJ2jtW/iSkhF+Lw/toxUbE4F+LwWLVjHGJxLsThbNsx3rE4F+JwVu3IoLE4x+VCLM6FODxW7dBzYj0ndh5Dz4mHbueo/03oEI99vQgsEpH5Trb2IuAhZ91DwEed2x8Fssk+9zecY+wzpsgJWinnAQNWVh2LdohIWEQKUreB0zIeb0JeCxER4BfAm8aYH/ZbN5rXYqj3ObN9l4rtKKDV6XaXzb5j0gYRmQv8HviIMebtjOVDvTfj0Y6ZznuBiByBHTuastl3rNrgPHYRcDwZ/1bG+LXYn/H+N6Fs4x2Hh3uc8YrFuRCHs23HeMTiXIjDWbVjAmJxLsThrNrhPL7G4ulBz4l7H0vPifWcOPV4ek5sG79/E2aUFT7z6YL9B1sD9AC7gf91llcBD2ds917syribsbvBpZaXAf8A3nGuS0fQhgGPMUAbQtj/2Iv67f/fwGvAq86bPWuEr8V+24FdffUV5/L6ZLwW2N23jPN81zuX947FazHQ+wxcA1zj3BbgVmf9a2RUuR7s38gIXoP9teEOYG/Gc1+7v/dmnNpxrfM4r2AXJnrXRL8Wzv3LgPv67TdmrwX2yU89EMeOFVdO9L+JqX4hB+LwUMcZoB3jFouzacNQ/74n8rVgnGLx/v7uJ+pvLot2jHsszqIN4x6Hs2mHc/8yNBbn9YUciMWDHWOANug5sdFzYvSceNrEYXEOopRSSimllFJKKTVpdIiHUkoppZRSSimlJp0mKJRSSimllFJKKTXpNEGhlFJKKaWUUkqpSacJCqWUUkoppZRSSk06TVAopZRSSimllFJq0mmCQimllFJKKaWUUpNOExQqJ4nI9SLyuoi8KiLrReTIYe4/T0Q2ZLHdLBH5s3P7hNTtXCEi94nIosluh1JqetJYbNNYrJSaLBqHbRqHpw/PZDdAqf5E5GjgTGC1MaZHRMoB3zg93OeAn4/TscfCfwJfBD422Q1RSk0vGov70FislJpwGof70Dg8TWgPCpWLZgGNxpgeAGNMozGmTkQOF5FnROQVEXlBRAqcrPCTIvKSc3lX/4OJiFtEviciLzrZ53/JWH0B8LcB9rlBRO4UkcdFZIuIfCpj3aXOcV4Rkf92lh0gIv9wlv9DROY6y+8Wkf8Ukcec4xzvHPdNEbk745inicizznP4rYhEnFVPAqeIiCYTlVITTWOxxmKl1OTSOKxxePoxxuhFLzl1ASLAeuBt4D+A47GzxVuAw51tCrF7AIWAgLNsEbDWuT0P2ODcvhr4qnPbD6wF5juXdRmPewLwZ+f2DcAzzvblQBPgBQ4B3gLKne1Knes/AR91bl8B/MG5fTdwHyDAOUAbsBw7ObgOWOUc/wkg7OzzJeDrGe16BDhsst8XvehFL9ProrFYY7Fe9KKXyb1oHNY4PB0vmoFSOccY0yEihwHHAicC9wM3AfXGmBedbdoARCQM/ExEVgFJYPEAhzwNWCEi73fuF2EH7g5gzxBN+YuxM9Y9ItIAzABOAn5njGl02tHsbHs0cL5z+7+B72Yc50/GGCMirwG7jTGvOW1/HftDoxo4GHhaRMD+4Hk2Y/8GoAo7eCul1ITQWKyxWCk1uTQOaxyejjRBoXKSMSYJPA487gSxfwXMAJt+FtgNrMTOwEYH2EaATxpj/rfPQpFDgcAQzejJuJ3E/nuRQdqxz1MY4DhWv2NazjGTwCPGmIsHOVYA6M7iMZVSakxpLO5DY7FSasJpHO5D4/A0oDUoVM4RkYOkb5XeVcCbQJWIHO5sU+CMQSvCziJbwEcA9wCH/F/g4yLidfZd7GSZ38bO1g7HP4APikiZc6xSZ/kzwEXO7UuAp4ZxzOeAY0TkQOeYIRHJzHovBl4fZjuVUmpUNBZrLFZKTS6NwxqHpyPtQaFyUQT4qYgUAwlgE/aYubuc5UHs7Okp2OPxHhCRDwCPAZ0DHO8O7KD7ktj9xfYA5xpjWkVks4gcaIzZlE3DjDGvi8hNwP+JSBJ4GbgM+BRwp4h8wTn+5dk+WWPMHhG5DLhXRPzO4q8Cb4vIDKDbGFOf7fGUUmqMaCzWWKyUmlwahzUOTztiTDY9c5SamkTkPOxiO1+d7LYMREQ+C7QZY34x2W1RSqnxorFYTQcicif2lJENxphlA6wX4BbgvUAXcJkx5iVn3enOOjdwhzHm5glruJoWNA6rXDElExTl5eVm3rx5k90MpZTqY926dY3GmIrJbsdE0DislMpFkxmHReQ47GKEvxokQfFe4JPYCYojgVuMMUeKiBu7C/6pQA3wInCxMeaN/T2mxmKlVC4aKhZPySEe8+bNY+3atZPdDKWU6kNEtk92GyaKxmGlVC6azDhsjHlCROYNsck52MkLAzwnIsUiMgu7S/4mY8wWABG5z9l2vwkKjcVKqVw0VCzWIplKKaWUUkpNvtnAzoz7Nc6ywZYPSESuFpG1IrJ2z56hZo5USqncowkKpZRSSimlJp8MsMwMsXxAxpjbjTFrjDFrKiqmxahCpdQUMiWHeCillFJKKZVnaoA5GfergTrAN8hypZSacjRBMUXF43FqamqIRqOT3RSlpp1AIEB1dTVer3eym5JTNC4plX80nk2oh4BrnRoTRwKtxph6EdkDLBKR+UAtcBHwoUlsZ07TzxqlcsdIPkM0QTFF1dTUUFBQwLx587BnrVJKTQRjDE1NTdTU1DB//vzJbk5O0bikVH7ReDa2RORe4ASgXERqgG8AXgBjzG3Aw9gzeGzCnmb0cmddQkSuBf4Xe5rRO40xr0/4E8gT+lmjVG4Y6WeIJigc7f98DE9FBb7583BHIpPdnFGLRqMamJWaBCJCWVkZWphsXxqXlMovuRrPjDG0N0VpruvggGXliCs/Yoox5uL9rDfAvw6y7mHsBMa4Msaw5aUXKCiroLSqGo/PN94POeb0s0ap3DDSzxBNUDja/v537DgmeGdXEVy5Ev/ChXhmzkRc+VlLVAOzUpND//YGp6+NUvkll/5mLcvQXNfB1lcaad3TDcYwZ2kZ7jxJUOSLt597GkRwu93MWLCIqsVLKJk1O6+SFbn071ap6Wwkf4uaoMjgmVUFQLKtnba//hUMuMIhAsuWEVi6FN/cubiCwUlupVJKKaXU9BGPJdm9rY1trzQS7YzjC7gpKPXTsbdnsps2RQlFFZVYySQN2zZT987GvE5WKKXyS352DRhHIoK7oADvrCq8VVVIMET3updovvuX7Lrx2zTefjudzz1HfPdu7J54ajA1NTWcc845LFq0iIULF/LpT3+aWCwGwOOPP86ZZ565zz6PP/44RUVFrFq1Kn159NFHAYiMcOjNj3/8Y7q6utL33/ve99LS0jLo9rfddhu/+tWvALj77rupq8u+UPZ5553HqlWrOPDAA/s8j2eeeWZEbQeoq6vj/e9//4j3H6mWlhb+4z/+Y9j7iQgf+chH0vcTiQQVFRXp9/uhhx7i5ptvHnG7+r+fE+Gqq67ijTfe2Gf53XffzbXXXjuhbVGjMx3jEsAJJ5zA3Llz+3xunXvuuen2jzbO/OEPfxjwb2Q8Zb4mmbZt28ayZcsGXP7rX/96WI+xbds2RISvfe1r6WWNjY14vd703/5g7cjWd77znRHvO1KD/Xu74YYb+P73vz/h7RlMd0eMTS818NRv3mbjM/WIQGFZgEDYq7+QTwCX2024uISiikpCRcU0bNvM2r/8gX/edRuvPPIwDdu2kHDip+prOn/WHHTQQaxYsYIlS5Zw7bXXDvl4w5HN+WM2n2Xz5s2jsbFx2I8/ktfjsssuIxQK0d7enl726U9/GhFJt+Fd73rXsNuS8vjjj4/qO8ZIDPU+jPTfaX+aoNgPl8+Hp7ISb1UVnhkzSDTsofWPD7Hnx7ew+zv/j5Y/PkT0rbewursnu6k5xRjD+eefz7nnnss777zD22+/TUdHB9dff/1+9z322GNZv359+nLKKaeMqi39g/PDDz9McXHxoNtfc801XHrppcDwg9GDDz7I+vXrueOOO/o8j1TwSSQSw25/VVUVv/vd74a932iNNEERDofZsGED3c7fxCOPPMLs2bPT688++2yuu+66EbdrMhIUd9xxBwcffPCEPqYae9M1LqUUFxfz9NNPA/bfd319fXrdaOPMZCQoMl+TbIwkQQGwYMEC/vznP6fv//a3v+WQQw4ZcTv6m4wExf7+vU0mYwyte7rZ8EQNzzywie0bmgiEvRSWBfD63ZPdvGlr32TFFl7660P8487bePl//8zurZuJ9+isGaCfNffccw+vvvoqr776Kn6/n3POOWfYxxhINueP43nOPNLX48ADD+SPf/wjAJZl8dhjj/U5Lx5NgmEyEhSjPY/PhiYohkFcLtxFRXirnN4Vfj9da9f29q647TY6nn6aeF0dxrImu7mT6p///CeBQIDLL78cALfbzY9+9CPuvPPOUX+57Ojo4OSTT2b16tUsX748/Uff2dnJ+973PlauXMmyZcu4//77+clPfkJdXR0nnngiJ554ItA3c/qrX/2KFStWsHLlyvSv/qlfkH73u9+xdu1aLrnkElatWsVf/vIXzjvvvHQ7HnnkEc4///z9tvfuu+/mAx/4AGeddRannXYazc3NnHvuuaxYsYKjjjqKV199Nf24H/nIRzjppJNYtGgRP//5z4G+vwYmk0k+//nPs3z5clasWMFPf/pTAL71rW9x+OGHs2zZMq6++ur0r6QnnHACX/rSlzjiiCNYvHgxTz75ZPqYxx57LKtXr2b16tUDBrfrrruOzZs3s2rVKr7whS9gjOELX/gCy5YtY/ny5dx///2DPuczzjiDv/zlLwDce++9XHxxb12wzF4Hl112GZ/61Kd417vexYIFC9IfKv1/Xbj22mu5++67B3w///73v3P00UezevVqPvCBD9DR0bFPey677DI+/vGPc+KJJ7JgwQL+7//+jyuuuIKlS5dy2WWXpbf7+Mc/zpo1azjkkEP4xje+kV5+wgknsHbtWgDuuusuFi9ezPHHH5/+sqfyw3SPSxdddBH33XcfAL///e/7bJcZZ+6++27OP/98Tj/9dBYtWsQXv/jF9HaZv4787ne/47LLLuOZZ57hoYce4gtf+AKrVq1i8+bNbN68mdNPP53DDjuMY489lo0bN+7Tnrvvvptzzz2Xs846i/nz5/Ozn/2MH/7whxx66KEcddRRNDc3A/Dzn/+cww8/nJUrV3LBBRek36vMX/vXrVvHypUrOfroo7n11lsHfP7XXXcdTz75JKtWreJHP/oR0WiUyy+/nOXLl3PooYfy2GOPDbhfMBhk6dKl6Rhw//3388EPfjC9PrMdg8Xc/r2tzjzzTB5//HGuu+46uru7WbVqFZdccgkA//M//8MRRxzBqlWr+Jd/+ReSyeQ+bTrhhBP47Gc/y3HHHcfSpUt58cUXOf/881m0aBFf/epX09ude+65HHbYYRxyyCHcfvvt6eWZ/95uuukmDjroIE455RTeeuutAV+DiWAlLfbsaGftw9t48S9b2bOjg3Cxn4ISP26Pnq7mklSyorC8gnBxMU07t/PSXx/in3f+Fy89/Ed2bX6HWHT6/nA33T9rUnw+H9/97nfZsWMHr7zyCgA//OEPWbZsGcuWLePHP/4xYH/+LFmyhKuuuoply5ZxySWX8Oijj3LMMcewaNEiXnjhBSC788dszplTuru7Of300/n5z38+4OuXqf/r0d3dzT/+8Q8OPfRQli9fzhVXXEFPz8BDzi6++OL08R5//HGOOeYYPJ7eKgupz9XHH3+cE044gfe///0sWbKESy65JH0+n/m+rV27lhNOOIFt27Zx22238aMf/YhVq1bx5JNPsmfPHi644AIOP/xwDj/88AHPUx9//HGOP/54PvjBD7J48WKuu+467rnnHo444giWL1/O5s2bAfjTn/7EkUceyaGHHsopp5zC7t2793kftm7dytFHH83hhx/ep6fhaGkNilFw+f24KisBMJZFoqmZNudLmfgDBA5eSmDpwfjmzsFdWDhp7dz1ne/Q8+a+J4ej4V+6hJlf+cqg619//XUOO+ywPssKCwuZO3cumzZtGvLYqRPIlAceeICFCxem7wcCAR588EEKCwtpbGzkqKOO4uyzz+Zvf/sbVVVV6S/Gra2tFBUV8cMf/pDHHnuM8vLyfdp400038fTTT1NeXp4+GU55//vfz89+9jO+//3vs2bNGowx/Nu//Rt79uyhoqKCu+66K/3hsz/PPvssr776KqWlpXzyk5/k0EMP5Q9/+AP//Oc/ufTSS1m/fj0Ar776Ks899xydnZ0ceuihvO997+tznNtvv52tW7fy8ssv4/F40m2+9tpr+frXvw7ARz7yEf785z9z1llnAXavjRdeeIGHH36Yb37zmzz66KNUVlbyyCOPEAgEeOedd7j44ovTJ98pN998Mxs2bEi37YEHHmD9+vW88sorNDY2cvjhh3Pccccxa9asfZ7vRRddxLe+9S3OPPNMXn31Va644or0iXp/9fX1PPXUU2zcuJGzzz57yK55n/rUp/q8n42NjXz729/m0UcfJRwO8+///u/88Ic/TL8Wmfbu3cs///lPHnroIc466yyefvpp7rjjDg4//HDWr1/PqlWruOmmmygtLSWZTHLyySfz6quvsmLFij5t/cY3vsG6desoKirixBNP5NBDDx20vWpwT/7mbRp37ptMGo3yORGO/eDiQddP97h08skn87GPfYxkMsl9993H7bffzo033jjgtuvXr+fll1/G7/dz0EEH8clPfpI5c+YMuO273vUuzj77bM4888z03+/JJ5/MbbfdxqJFi3j++ef5xCc+wT//+c999t2wYQMvv/wy0WiUAw88kH//93/n5Zdf5rOf/Sy/+tWv+MxnPsP555/Pxz72MQC++tWv8otf/IJPfvKTfY5z+eWX89Of/pTjjz+eL3zhCwO28+abb+b73/9+ujfED37wAwBee+01Nm7cyGmnncbbb79NIBDYZ99UcmfmzJm43W6qqqoG/SVtoJg7mJtvvpmf/exn6Tj75ptvcv/99/P000/j9Xr5xCc+wT333DNgDw2fz8cTTzzBLbfcwjnnnMO6desoLS1l4cKFfPazn6WsrIw777yT0tJSuru7Ofzww7ngggsoKytLH2PdunXcd999vPzyyyQSCVavXr3P38h4i/ck2b21la2vNhLrSuB16kvoEI784HK7CRUVA2BZSZrra9m9bQsuEUqr5zL7oIMpq56DPxSelPY9dvftNGzfMqbHrDxgASdedvWg66f7Z00mt9vNypUr2bhxI4lEgrvuuovnn38eYwxHHnkkxx9/PCUlJWzatInf/va33H777Rx++OH8+te/5qmnnuKhhx7iO9/5Dn/4wx/2Ofb+zh8HO2cGO9Fz0UUXcemll3LppZfywAMP7PP6DfV6RKNRLrvsMv7xj3+wePFiLr30Uv7zP/+Tz3zmM/u0c9GiRfzxj39k79693HvvvXz4wx/mr3/964Cv18svv8zrr79OVVUVxxxzDE8//TTvfve7B9x23rx5XHPNNUQiET7/+c8D8KEPfYjPfvazvPvd72bHjh285z3v4c0339xn31deeYU333yT0tJSFixYwFVXXcULL7zALbfcwk9/+lN+/OMf8+53v5vnnnsOEeGOO+7gu9/9bvpzM+XTn/40H//4x7n00ksH/XFgJDRBMUbE5cJdWJhORJhYjOiG1+l++WUw4KmsILB8Of4FC/FWz8Y1xYsLGWMGPLkYbHmmY489tk932oGO8ZWvfIUnnngCl8tFbW0tu3fvZvny5Xz+85/nS1/6EmeeeSbHHnvskI/zz3/+k/e///3poF1aWjrk9qnaCv/zP//D5ZdfzrPPPpv1uONTTz01ffynnnqKBx54AICTTjqJpqamdCA855xzCAaDBINBTjzxRF544YU+H1SPPvoo11xzTTrzmjrmY489xne/+126urpobm7mkEMOSScoUhnuww47jG3btgEQj8e59tprWb9+PW63m7fffnu/z+Gpp57i4osvtgtlzZjB8ccfz4svvsjZZ5+9z7YrVqxg27Zt3Hvvvbz3ve8d8rjnnnsuLpeLgw8+OJ2dzdZzzz3HG2+8wTHHHANALBbj6KOPHnDbs846CxFh+fLlzJgxg+XLlwNwyCGHsG3bNlatWsVvfvMbbr/9dhKJBPX19bzxxht9EhTPP/88J5xwAhUVFQBceOGFWb12KjdM97jkdrt597vfzf333093dzfz5s0b9Lgnn3wyRUVFABx88MFs37590ARFfx0dHTzzzDN84AMfSC8b7JelE088kYKCAgoKCigqKkrHreXLl6d7l23YsIGvfvWrtLS00NHRwXve854+x2htbaWlpYXjjz8esJO0g538ZXrqqafSiY4lS5ZwwAEH8Pbbb/f5m085/fTT+drXvsaMGTO48MILhzzuQDE3W//4xz9Yt24dhx9+OGD/wlfp/BDSXyr2Ll++nEMOOSSdLF6wYAE7d+6krKyMn/zkJzz44IMA7Ny5k3feeadPguLJJ5/kvPPOIxQK9TnmROhqi1HzVjM1G/diWYZg2EtB2b7JIZU/XC43oUI7bhjLom3Pbpp2bgegZNZsO1kxZy7Bgsn70W4iTPfPmoHaDHbMPe+88wiH7WTV+eefz5NPPsnZZ5/N/Pnz+5yXnXzyyelztsHi6P7OHwc7Zwb7fPuLX/xiuufacF+/t956i/nz57N4sf2jyEc/+lFuvfXWARMUqed633338fzzz/Nf//Vfgx73iCOOoLq6GoBVq1axbdu2QRMUA3n00Uf7DLdsa2ujvb2dgoKCPtsdfvjh6c+MhQsXctpppwH265DqTVhTU8OFF15IfX09sViM+fPn7/N4Tz/9dPo7zUc+8hG+9KUvZd3WoWiCYpyIz4fH+aM3xmC6uuh47DE6/vkYiOCfP4/AsmX4DjgAz4wZ4zqV6VA9HcbLIYcckv4Hm9LW1sbOnTtZuHAhTU1NIz72Pffcw549e1i3bh1er5d58+YRjUZZvHgx69at4+GHH+bLX/4yp5122oC/pKdk80HR3+WXX85ZZ51FIBDgAx/4QJ8uWkNJBePU4/aXakf/9vS/P1Cbo9Eon/jEJ1i7di1z5szhhhtuIBrtHQfq9/sB+8tJqgbGj370I2bMmMErr7yCZVkD/mLY33CLwp599tl8/vOf5/HHHx/y/U61L/MxPB4PVsYwqczn079Np556Kvfee+9+25N6HJfL1ecxXS4XiUSCrVu38v3vf58XX3yRkpISLrvssgEfV3/VGxtD9XQYLxqX7J4A5513HjfccMOQx8z8G8mMHZltG+zv0rIsiouL070Csn2czL/N1N8l2F15//CHP7By5UruvvtuHn/88T7HGMlrltovWz6fj8MOO4wf/OAHvP766/zpT38adNuBYu5wYtpHP/pR/t//+3/7bdP+Ytrjjz/Oo48+yrPPPksoFOKEE06Y9JhmjKG1oZvtrzfRuLMdcUEw4tMhHFOQuFwECwoJFhRijEVnSzMbHn8EAxSWV1K95GDK5hxAuLhkXP8NDtXTYbzoZ02vZDLJa6+9xtKlS/vUPeovm8+CofYZKJ4P9RyPOeYY/vrXv/KhD30IERnR6zccF110EatXr+ajH/0oriG+8w322Zv5GTLY5wfYn7/PPvsswf3MOpnN6/3JT36Sz33uc5x99tk8/vjjg543jMffr34iTAARwRUO4505C++sWXgqKojX77KLbf70Z+y68Ub23ncf3a+8QqK5eUrMDnLyySfT1dWVzq4mk0n+7d/+LV3NdjRaW1uprKzE6/Xy2GOPsX27nZ2vq6sjFArx4Q9/mM9//vO89NJLABQUFPSpnpvZxt/85jfpD4r+3dsG2reqqoqqqiq+/e1v96ldMBzHHXcc99xzD2CPAysvL6fQ6Xnzxz/+kWg0SlNTE48//nj6V7SU0047jdtuuy0dPJqbm9OBqry8nI6OjqyKA7W2tjJr1ixcLhf//d//PeAY5/7P/bjjjuP+++8nmUyyZ88ennjiCY444ohBH+OKK67g61//ejojPhwHHHAAb7zxBj09PbS2tvKPf/xjwHYdddRRPP300+kuk11dXSPu0dDW1kY4HKaoqIjdu3cP+AvskUcemU64xONxfvvb347osdTk0Lhk/zr35S9/uU9dmOGYMWMGb775JpZlpX+Z79+mwsJC5s+fn/77MMakxx+PRHt7O7NmzSIej6djZ6bi4mKKiop46qmnAAbcpn8boW8sfvvtt9mxYwcHHXTQoO34t3/7N/793/+9Tw+EbM2bN4/169djWRY7d+5Mj6kG8Hq9xONxwH7/f/e739HQ0ADY73/q39Jwtba2UlJSQigUYuPGjTz33HP7bHPcccfx4IMP0t3dTXt7+5CJl9FIJix2bW3l+Ye2sPav29hb30mkxE+kOKDJiWlAxEUgUkBhRSWF5RXEujt586n/48l7f8kTv76bTWufp21Pw5Sp36afNbZ4PM6Xv/xl5syZw4oVKzjuuOP4wx/+QFdXF52dnTz44IP77akwGgOdM6d861vfoqysjE984hPA4K9fpszXY8mSJWzbti19/vnf//3f6V58A5k7dy433XRT+vGGa968eaxbtw6gT/Kr/3t02mmn8bOf/Sx9P5sfCgbT2tqaLub5y1/+csBtjjnmmHRtq8E+e0dCPxUmgbjduIuL7WKbs2bhLigkuvEt9t53Pw3f+z4NN/87LX/8I9E33iDZ1jbZzR0REeHBBx/kt7/9LYsWLWLx4sUEAoF0tfJEItEne5cpNf4uden/hfuSSy5h7dq1rFmzhnvuuYclS5YA9jjiVGGxm266KV0o7Oqrr+aMM85IFwhKOeSQQ7j++us5/vjjWblyJZ/73Of2actll13GNddcky6Ik3r8OXPmjHhWhxtuuIG1a9eyYsUKrrvuuj5/9EcccQTve9/7OOqoo/ja175GVVVVn32vuuoq5s6dmy5q9Otf/5ri4mI+9rGPsXz5cs4999x9khoD+cQnPsEvf/lLjjrqKN5+++0+PTxSysrKOOaYY1i2bBlf+MIXOO+889KPe9JJJ/Hd736XmTNnDvoY1dXVfPrTnx7GK9Nrzpw5fPCDH2TFihVccsklfeo8ZL6fFRUV3H333Vx88cXpoqOpYnxf//rXeeihh7J+zJUrV3LooYdyyCGHcMUVV6SHjWSaNWsWN9xwA0cffTSnnHIKq1evHtHzU5ND45L9Gnz+85/fZzxytm6++WbOPPNMTjrppD71Zy666CK+973vceihh7J582buuecefvGLX7By5UoOOeSQdCG3hx56aMhfpQZy4403cuSRR3LqqaemX9f+7rrrLv71X/+Vo48+etBfjlasWIHH42HlypX86Ec/4hOf+ATJZJLly5dz4YUXcvfddw/6/oP93nz0ox8dVttTjjnmmHQX5s9//vN9YsfVV1+djnUHH3ww3/72tznttNNYsWIFp556avpXx6uuumqfWkFDOf3000kkEqxYsYKvfe1rHHXUUftss3r1ai688EJWrVrFBRdcMOZfFnq64mx7tZGnfvsOG/6vlng0SUGpn1ChT3ujTVMigj8UprCigsLyCoyVZNOLz/LM737NY7+6g43PPEFzXQ3JEcx6lium+2fNJZdcwooVK1i2bBmdnZ3p+L969Wouu+wyjjjiCI488kiuuuqqca3jNdA5c6Yf//jHRKNRvvjFLw76+mXKfD2MMdx111184AMfYPny5bhcLq655poh2/Mv//IvfeqJDMc3vvENPv3pT3PsscfidvfOZnTWWWfx4IMPpotk/uQnP0l/xzj44IO57bbbALuw5lVXXTWsx7zhhhv4wAc+wLHHHjvoOcMtt9zCrbfeyuGHH75P3Y7RkKnwa31/a9asMcP5EAeove7L9swcOfCBaUWjWO3tGOcXFXdZGcGDl+JbuBDv7Grckf0XGnrzzTdZunTpeDd1xG655RZqa2v57ne/O9lNGbZrr72WQw89lCuvvHJMj3vDDTf0KXSj8ttAf4Miss4Ys2aSmjShBorDGpfGz3jFJaVgeH+7xhg69vaw881m6je3AoZgxIvHO/ZThLY3RznhQ0twe7P/vW06xWEY/jmxMYa//cePKRqk9sl4SsRidLe3YywLl8fDzIUHMnPhYkpmVeH1Z1+fRD9rxo9+1qiRGO45sdagyEGuQACXUxPAGIPp7qbj6WfgqacwxuCtrCSwdCm+BQvwVs3OKmGRS6688ko2bNjAb37zm8luyrAddthhhMPhfarYKqXym8YlpUbHSlo013ey7dUmWhq6cLldhIt8uFyT/8OPyg8en48CZwhVMpFg95ZN1L31JiIuyqrnMmvxEkpnVxOMFOznSLlLP2uU2j9NUOQ4EUFCIVzOmLV0wc2nnoYnnrBnCKmoILDkILuHRVUV7oLcDty/+MUvJrsJI5Ya/zUe9le0TqmxJiI3AucAFtAAXGaM2WfuRBEpBu4AlgEGuMIY8+wENnXcaVxSamRi0QS7t7ay7bUmnSZUjRm3x0O4uASwC/+17tlFY812MIaC8gqqFi+lrHouBaVl41pofqzpZ41S+6cJijwjIkg4jMupGZBOWDz9DDxpFwhzl5ZgnXQSVnc34vWC260nCkqpgXzPGPM1ABH5FPB1YKBBlLcAfzPGvF9EfMDoqnwppfJeZ0sPNW/tpfbtvZikIRDRaULV+HClZwSxz3tj3d289eyTAPiCIWYduJjKeQsomjELj9c7ya1VSo2WJijy3IAJi2gUE4uRcCrzisuNBPyI3494vfZFExZKTXvGmMwqvGHs3hF9iEghcBxwmbNPDIhNRPuUUrnFsgx76zvZ/noTzXWduNxCqMCLy50/v2Cr/GYX2Qzhd3oWJ2Ixdrz+KttfXY/L7aJ87jx8s+dhJZO43GNf90QpNf40QTHFiAgSDILLjXh99kJjYUWj0NUFiP2fz4crEEB8PsTjQTSIKzUtichNwKVAK3DiAJssAPYAd4nISmAd8GljTOcAx7oauBrsKbWUUlODMYaat/ay/bVGujvieH0uHcahcoLH56Og1K5bYSWTNNfVUFo2g/amRlweDz5/ALfPh9vj0X+vSuUJTXlPB+JC3B7E67N7T3g8EE+QbG0jsaeReH098V27SLS0YHV1YcXjTMXZXZSajkTkURHZMMDlHABjzPXGmDnAPcC1AxzCA6wG/tMYcyjQCVw30GMZY243xqwxxqypqKgYp2eklJooyYRFtDNOT2eCt56tB2MoLAsQLNBpQlXucbndhAqLcLncuD0ejGUR7eygc28z7Y176GprJd4TxbKsyW6qUmoI496DQkTuBM4EGowxywZYfwnwJeduB/BxY8wrzrrTscc+u4E7jDE3j3d7p6qmX/yC+O7dQ29krN4O3oLdC8PtQlxuEAGXOCvAO2sWlZ/+1JCHu+KKK/jzn/9MZWUlGzZsGHS7W265hZ///OcYY/jYxz7GZz7zGQBeeeUVrrnmGjo6Opg3bx733HMPhYWFPP3003z84x/H7/dz7733cuCBB9LS0sKFF17I3/72twFPmuLxOF/72td44IEH8Pv9hEIhvvnNb3LGGWcwb9481q5dO+gcv0rlM2PMKVlu+mvgL8A3+i2vAWqMMc8793/HIAmK4Xr+oS20N0fH4lAAFJQGOPLsBUNuo3FJqaEZY0jGLWLRBImY/UVOXGh9CZV3XC4XuFy8+KcHaG9qAmPSp7kulyBuDy5xIcOcaaawvJJjLvzwkNvoZ41SI5d1DwoRWSsi/yoiJcN8jLuB04dYvxU43hizArgRuN15PDdwK3AGcDBwsYgcPMzHVo747t14Z80a+lI1G+9s51JVhXdGJZ7SMtzFxbiLinAXFuEpLcFTUUG8pgaTTA7Z0+Kyyy7jb3/725Dt2rBhAz//+c954YUXeOWVV/jzn//MO++8A8BVV13FzTffzGuvvcZ5553H9773PQB+8IMf8MADD/Cd73yH//zP/wTgxhtv5Ctf+cqgv+h87Wtfo76+ng0bNrBhwwb+9Kc/0d7ePpKXUqlJMYoYPNQxF2XcPRvY2H8bY8wuYKeIHOQsOhl4Yywev705SmFZYMwu2SQ7NC4pNTDLMsSiCTr39tDVFiMZt3B5BLfHRerHCWUbTTwWkdNF5C0R2SQi+yR7RaRERB4UkVdF5AURWZaxbpuIvCYi60Vk7Wifx3TR0dxEYXkFhRWVFDmXSGk5ocIiAgUFBMIRQkXFRErL7G0qZwx5aWts2O9j6meNUiM3nCEeFwFVwIsicp+IvEey6N9njHkCaB5i/TPGmL3O3eeAauf2EcAmY8wWpyjbfdjT4akJIfZPJm5370UEk0hiolGsnijx+noSu3aRaGoi2dGBFY32SVocd9xxlJaWDvkob775JkcddRShUAiPx8Pxxx/Pgw8+CMBbb73FcccdB8Cpp57KAw88AIDX66W7u5uuri68Xi+bN2+mtraW448/fsDH6Orq4uc//zk//elP8fv9AMyYMYMPfvCDY/JKKTVBRhSD9+NmZ7jHq8BpwKcBRKRKRB7O2O6TwD3OdquA74zycSeNxiWl+komLKIdcTr2Rol2xAFwe1y43C5EExODGVE8zvLHt68A650f7i7F7kmc6URjzCpjzJpRP4tpTERwuVx2LwsRrESCeE8PPV1dxLq7ScTiWEmLkY541s8apUYu6wSFMWaTMeZ6YDF2V+A7gR0i8k0RGfovMHtXAn91bs8Gdmasq3GWDUhErnYy2mv37NkzRs1RfYiAy0laiMuewlQEq6eHZEsLicYmO2lRX0+isZFkW5tdnNOYQXtaLFu2jCeeeIKmpia6urp4+OGH2blzZ3rdQw89BMBvf/vb9PIvf/nLXH311fz4xz/m2muv5frrr+fGG28ctNmbNm1i7ty5FBYWjvELoqYrM8S/6XF8zDGPwcaYC4wxy4wxK4wxZxljap3ldcaY92Zst96pLbHCGHNuRlJ5StK4pKY6YwzxWJLO1h46W3qI9SRwuezeEsPt7j5ZuttjdLb0TMpjjyIeZ/Pj28HAP5zH2QjME5EZY/4kVJqIIE6ywuVyYYwhEY8Ri3bT09VJLBolmUhgWSNPWAxEP2uUGtiwimSKyArgB8D3gAeA9wNtwD9H2xARORE7QZGqRzHQJ+SgYUGLs00G2bcAp9cHLhcmFifZ3k5ibwsmkSBeV0d8924Se/eS7OzE6unBJJMsWbKEL33pS5x66qmcfvrprFy5Eo/HLo1y5513cuutt3LYYYfR3t6Oz2fPSrJq1Sqee+45HnvsMbZs2UJVVRXGGC688EI+/OEPs3t/tTaUyoIxBpNIYPX0YHV1kWxrI9HURHz3buJ1dVid+0xiMe7GMwarXkuXLtW4pKYkyzL0dCfo2NtDd1sMK2HsYRxuV14UvYzHkuze2sbrT9Wx4Yk6dm1tm7Si3iOMx9n8+PYKcL7zGEcAB9Dbu9gAfxeRdc6sSYO1TX+0G4XM3hXiEkwySbynh1h3N7HuLpJxu7eFZSVH9Tj6WaPUwLIukiki64AW4BfAdcaYVNr6eRE5ZjSNcIL8HcAZxpgmZ3ENMCdjs2qgbjSPoyaIuMANgtueMUTEvrYstm/axPmXXQbAxz78Ya6+9KNcetZZfPSCCxCvj69+8waq587FGMOSJUv4+9//DsDbb7/NX/7ylz4PY4zh29/+Nvfffz/XXnst3/zmN9m2bRs/+clPuOmmm9LbHXjggezYsYP29nYKCgom7GVQuc0YY/fuSSYhmbSvEwlMPI5JJDCJJGDsnkPGAHahWBGXfX+Cq4CPZwye7nbu3MlZZ50FwDXXXMM111zDlVdeyZVXXgnAV77yFaqr7e8HGpdUvjHGYCXs+hLxHvsLlcstuDz5MZGbZRlaG7porOmktaELYyBU6GPO0hL8ocmZOnIU8TibH99uBm4RkfXAa8DLQMJZd4wxpk5EKoFHRGSjM5S67wGNuR2nptuaNWt0WrZRkNRnv3PfGEMymaCrtQUAt8eDx+/H43WmMnUN/nelnzVKZWc4s3h8wBizJXOBiMw3xmw1xpw/0gaIyFzg98BHjDFvZ6x6EVgkIvOBWuzxfh8a6eOoySbgcjN37gGs/edjGcsNDbt3U1Fayo6at/j973/PE3/4A/G6OvbsbWHG7CqM282N3/wmV3/sYxjLSgf/X/7yl7zvfe+jpKSErq6udLa7q6urzyOHQiGuvPJKPvWpT/Ff//Vf+Hw+6uvr+cc//sGHPzx0FWaVv/okICzLTkIkEvYlnsAkE06SQexTRmMnI8QZyiReD4MWhrMm5ZfGcYnBCubMmcP69ev7LGtoaKCyspIdO3bw+9//nmeffbbPcsuy+Pa3v80111zTZz+NSypXGMuQiCfp6U5iJSznY1jyoqeEMYbO1hhNNR001XWSjFt4/W4q5xVSXh0hVGj/mjyWswAN00jj8X5/fDPGtAGXO8cU7GLyW511dc51g4g8iD1kZJ8EhRo/du8Kwe30dLAsi57OTnqwe1W6vV68fj9ur2+f3j36WaNUdoaToPgdsHqAZYcNtZOI3AucAJSLSA32FHZeAGPMbcDXgTLgP5wPzYQzVCMhItcC/4s9zeidxpjXh9FelcE7Ywbx+voxPd7+fPiaf+GJZ56hsbmZ+Yeu4utf+AKXf+iSflsJF37sYzQ178Xr9fCTm2+mtKISMNz/4O/5z7vuAuDcM87gI6efQbyuDnG56YrHuPsXv+BvD/0JKxrlM5/6FBdccAE+n4977713n7Z8+9vf5qtf/SoHH3wwgUCAcDjMt771rTF4JdRkMZYFlmVfJ5P2dSoB4VxSSQd7h8xeEIKkir/mTxG4EcXgXFZQGqCtaWynGd2fiy++mMcff5zGxkaqq6v55je/mf71KtMFF1xAU1MTXq+XW2+9lZISu1j/vffey6233grA+eefz+WXX57ep6uri1/+8pfpX70+97nPaVxSE85KWsR6ksS77aLV4hJcHsmLgpc93Qmaajtoqukk2hlHXELJzBBls8MUlQdzqT7GSOPxfn98E5FioMupUXEV8IQxpk1EwoDLGNPu3D4N0ICRhUhpGe1NYzfUJVJalr6dmso0xUomiXZ0cOW/XMNTzzxDU3Mz1bNn841vfIOrPvaxfRKE+lmj1L5kf2P3RGQJcAjwXeALGasKgS8YYw4Zv+aNzJo1a8zatcObfan2ui/jrarKi18WsrH7iCM4aN68yW7GOLB/FccY+wtp6ktnap0znCR1weOxv4i6XL3XU+Q9nqqMM3wilYDAsuzZYRJJSDqJh1RCApwhGJB+/8WZgcYljFfywSTiuAsKcA9RdOrNN99k6dKlfZaJyLrhVl7PxxgMA8fhgV4TpdToGWNIxi1i0QSJmB0bx7K3xDub3yZWFx6TY/WXTFjs3dVFU01HOmEZKfVTPjtCyawwHu/gXebbm6Oc8KEluIfYpr+RxOGMfUcdj0XkvcCP6f3x7SYRuQbsH+5E5GjgV0ASe0rnK40xe0VkAfCgcxgP8GtjzE37PEA/wz0nNsbwt//4MUWVlVnvk2tC8w/iwAXzJ7sZGOxzVWM537UEPF4fHr8ft8drDwnRc1I1DQz3nDibHhQHAWcCxcBZGcvbgY+NrJlKjZTYX0gFxOUeYL395dbq6QFnBpH+v6CL25VOXIjHA263PWzE5UpfayJjbKWSSX2SDpYBK6P2QzIJSQuTKjrV530jI/lgDxeS/Or9MBoag5VSA7IsQ6InSU93AmMZOzzmQW8JYwztTVEaazrYu6sLK2nwhzxULSqmrDpMIOSd7CYOZtTx2BjzMPBwv2W3Zdx+Flg0wH5bgJXDbrGaNILY56pO/sxgSCbiJGKx1AZ2wsLnx+314HYPXcNCqelivwkKY8wfgT+KyNFO0FQqhzlfXgdd7/TASCSw4vF0b4x0HQJIfyHu0/PC7e69n0pipGoVpL80T+2kRqqmA5bVezvzfuZQi/S1k3BIJYpSHV5SPR6c1733NRTE7WWaJB6yojFYKZXJGEMyYYj3JIhHnaKXrvwoetndEbfrStR2EIsmcXuE0qow5dURIiX+nP8M1XisRmO/CQvsGhYenw+P14vL47WHkCg1zew3QSEiXzTGfBf4kIhc3H+9MeZT49IyNUrGHnua4x/2Ey+jB8aQ22V8+U5aGGIZyYzUcXo3TRfhdubSTiUsEGdOeWdZep24er+Yp46Veq8y37PBbu/T3AGGaqWWDXBtUm1OJRaM6U0yWAZj7GtMxv3Mx8hsS//l4vx2l7rtSYWZ6fNvcSynvZtqMVjjklIjYyxDPJYkFk1gJcyEFb0cbTxLxJI013XSWNtBZ4v9RayoIkj10gglM4K43PnzBWyqxeOpKz/OgfsnLMCuYWEX3bS53G4nYeHD5fHgcrtz/nkplWkknyHZDPF407keXlEHNam8nV3s7eygJBzRQDYivYkM514W+iUCkkkgiTVYoiDzsVLTWaYOM1ZvWWaPhdRMFf2l/31kJG4ykyUuQWT6JRlGyhhDU1MTgcD+CzZmacrE4EAgQFNTE2VlZRqXlMpSMmER77ETExgmtOilMYaWtr2Y+PCSCPbUoN001XbQstueGjRY4GXO0hJKq8L4AsOp0Z5Tpkw8nsqsnh5a2tspLijIu8+a/kU3jbGIRbuJdXcD9g9bbp8Pj8+e1tTl9mgvC5WzRnpOnM0Qjz85178cYdvUJCh6+y1agD3hEPqlUqkxZiWRQADXIAE3EAik5zIfrakUg6urq6mpqWHPnrGrpq7UVGSMwUoaEnELk3SGHbpgMj7PTdxFvMm//+2MoastRmNNB811nSRiFh6fa5+pQfPZVIrHU1l0Vw17gCa/n6l2Dmyc3q+Zv0qLy4Xb7fSucHpY5FtiRk1dIzknzjqFLSKPYM/73OLcLwHuM8a8Z1iPqCaEO56g9HWdlVWp8RDftYuCk0+i4KSTJuwxp0IM9nq9zJ8/+ZXVlcpVXW0xdm1uYefGvcRjSXx+H/5Qblf6j3UnaKrrpLGmg2hHHHFB8YwQ5bMjFFXk1NSgY2YqxOMpzUoSrds+2a2YEMYY4j1RYl1dWJYFCC63i+IZMymdPZeiikoipWUEIvnXm0RNX8PpY1eRCsQAzpRH+TsHkVJK5ReNwUpNQcmkxd76Tna80czeuk5wQTDiIxjJ2ZkseqcGre2grdGZGrTEzwHLyyidFcLjHWiWrSlF47HKCSKCLxDEFwiml1nJJB17m2muqyM1/Njj9VE8cxalVdUUllcQLinVpIXKWcNJUCRFZK4xZgeAiBxAetC9UkqpcaYxWKkppLsjRv3mVmrebCYeTeLxu4iU5u5MFsYY2pujNNV00lzfiZU0+IIeqhYVUTY7QiCcuwmVcaDxWOUsl9tNIBwhEI6klyUTCVr37KZxx/b0qBe3x0vxjJmUpJIWxSUECwtxuaZ8glHluOEkKK4HnhKR/3PuHwdcPfZNUkopNQCNwUrlOStp0Vzfxc43m2iq7URECBZ4cvrLfbQjTmOtMzVodxJXamrQ2ZGcTqiMM43HKq+4PR6CkQKCkYL0smQiQXvTHppqa0jl11ziIlJWTsmsKooqZxAqKiFcVIx37Ap/K7VfWScojDF/E5HVwFHYubfPGmMax61lSiml0jQGK5W/uttj7N7axo43moj3JPF4XRTk8Jf7RNyZGrSmk84We8LDooog1QeFKZ4Zwp1HU4OOB43Haipwezy4IwUEMpIWlmXR093JzjdeY/ur6xGXYCwLfyhMYeUMimfOIlJSRqioiFBhEW5P7iZXVf7ab4JCRJYYYzY6gRigzrme63Rve2n8mqeUUtObxmCl8pNdWyJ/ektYlqFtTzeNNR20NHRhLAhGvFQvKaFsdl5PDTpmNB6rqc7lcuEPhvAHQ+llxhiSiTgtu+rYs30b6dFMBoIFBRRWzKCocgbhklJChUUECwrx+PJ/1h41ebL5tPkcdre1HwywzgATV8ZeKaWmH43BSuWRrrYYu7bYM3Ekokk8vtztLZGaGrSppoOmzKlB5xZQ5kwNmovtnkQaj9W0IyJ4vD48Xh/B3s4WGGNIxGI01+5k95ZN2KFCMMbCH45QUFpGYUUlkdJyggUFBAoKCYTCiGt698BS+7ffBIUxJjWm7gxjTDRznYjogCSllBpHGoOVyn3JuEVzfSc73mhi7+4uROzeB8Ec7S0RiyZoqu2kqbaD7nZnatDKEOXVEQorgrim4NSgY0HjsVK9RASv34/X7yeYsTzV46KtsYGmuhpM0sLOXhhEhFBRMZHSMgrKKggXFxOIFBAIR/CHw1qgUwHDK5L5DLA6i2VKKaXGnsZgpXKIMYbOlh7qN7dS+9ZeEgkLn89NQUlu9pZIJi1adnXRWNtB2x77u3W42M8By0oprQpPh6lBx5LGY6UGkdnjon/WzliW3euiZicNWzZhDHadC2MPGwlGCgiXlBIpLSNcXEogEsEfChMIh/EGgjkZW9XYy6YGxUxgNhAUkUNJT05DIRAadEellFKjpjFYqdwSjyVp3NnOzjeaaWuK4nIJwQIvIU/udVs2xtDR3ENjbYc9NWjC4Au6mXVgEeWzIwQiudnDI1dpPFZqdMTlwhsIDDgriDEWiXictj27aa7diZVIIi4BEYxlELeLYEEhoaJiwkUlhItL8IfD+IMhfKEQ/lBIi3ZOEdn0oHgPcBlQjT3mLhWM24GvjE+zlFJKOTQGKzXJjGVobeymflML9ZtbMZbBF3DnbG2JaGecptoOmmo66elO4HILpbPClFWHKSgN5GSb84TGY6XGiYgLr8+P1+cfcL1lWSTjcdoadjkJjIQ9dEQEMQZjDB6fn0CkgGBhoV2ws7AQfyiMLxDCFwziDQTwBYK43NpjLJdlU4Pil8AvReQCY8wDE9AmpZRSDo3BSk2eaGecPTva2PnGXro7YojbRajAiysHp9lMxJM013fRVNNBx157atDC8gBVi4spmRnCnYM9PPKNxmOlJo/L5cLl1LwYiDEGy0qSiPfQsqueppodJBMJBLF7YjjbGMvg9fvxh8MEwhG7BkbEvvb6A/Yl4Mfjsx/L48vNRPRUNpwaFNUiUoidJf459ji764wxfx+XlimllMqkMVipCZCaHrT2rb001rRjgGDIQ0Fp7tVATPXsaKzpoGW3PTVoIDU1aFUYX3BqTg1qzx5g4Qxbnwwaj5XKMSKC2+3B7fawT/GLDMYYrGQSK5mgvbmR1oZdJBMJLMtCcHpk2FumJlTF6/fjC4bwBYL2cJJgCF8ojD8UwuOz6224vT48Pi8enx+P14vb68Xl9mhyYwSG88l1hTHmFhF5D1AJXA7cBWgwVkqp8acxWKlxkip4uWtLG7Vv7yXRY08PGin2p395yyVdbTEaazpoqusg0WPh8bqomFtA+ewIoaKpOTWoZRliXQnisSQAwQIfB66uwOWZlOc64ngsIqcDtwBu4A5jzM391pcAdwILgajzWBuy2VcptX8igtvjwe3x4B24M0YfmQmNrrYWOvY2YTkJDWNZzkFdZIZdYwwYe9YSj89vJzH8fry+AB6/D68/YPfQCNhDWtw+H26PB5fbk3HtxuWxEy7iduN2u51rD+JyTck4nzKcBEXqVXgvcJcx5hWZyq+MUkrlFo3BSo2xWDRhF7x8cy/tzU7By4iXYA4Wj4xHEzTVddJY40wNKlA8I0TZ7AhFlVNzatBE3CLaGbcL5IlQMivEjHmFFM8IEyzwTuYJ+ojisYi4gVuBU4Ea4EURecgY80bGZl8B1htjzhORJc72J2e5r1JqjGUmNIbLGAsraScy4j1RYl1dWFYSY1npBIfdcwNwem+kpmPtdyS7x5ixr0XA5fbgcrudhIadzEjfd3lwedy4XG772u3cdruR1DKXC3G5cLnczrXLrs0hYvckcUlvjxKx2yci6egn6RtCYUUlocKiEb7C+xrOK71ORP4OzAe+LCIFgDVmLVFKKTUUjcFKjQEradGyu5u6TXvZva0NLPAFPTlZ8NJKWuzd3U1TTQetjd1gIFzs44BDnKlBfVOr0Jsxhlg0Saw7AYA34KZqcQkV1RGKKoK59HxHGo+PADYZY7YAiMh9wDlAZpLhYOD/ARhjNorIPBGZASzIYl+lVA4RcY1L/R/jFAU1xgLLpHt5JBNxYlG794axjD1IxblO9eroc01vb4/0bbvhiAGTzkcIBuMkJZwMiQEEYt3dLDvhFBasPnzMnt9wEhRXAquALcaYLhEpw+7SNiQRuRM4E2gwxiwbYP0S7G5xq4HrjTHfz1i3DXt8XxJIGGPWDKO9Sik1lYwoBiulUkM4YjRsa6Xmrb3Ee5K4PS7CRf6c63lgjKFjbw9NNfbUoMmEwRtwM2tBEWXVYYIR32Q3cUxZSYtoV4Jk3P5+X1AaYO4hZZTOCufsEBtGHo9nAzsz7tcAR/bb5hXgfOApETkCOAB71pBs9gVARK4GrgaYO3duFs1SSuUTEac3Ay57wNckam/cM+bHzDpBYYyxRGQrsFhEhlMp6m7gZ8CvBlnfDHwKOHeQ9ScaYxqH8XhKKTXljCIGKzVt7TuEwy4iGQjn3hCOaFecpppOmmo76OmypwYtmRmivDpCQdnUmho0EUsS7UpgLHC5oaw6wox5RRTPCOHPg8Keo4jHA72J/Ut93gzcIiLrgdeAl4FElvum2nc7cDvAmjVrJq+UqFJKjUDWnwIichXwaews7nrgKOBZ4KSh9jPGPCEi84ZY3wA0iMj7sm2LUkpNNyONwfs55o3YXYQtoAG4zBhTN8B2nwWuwj4Zfg243BgTHenjKjWekkmLll1d1L69l8adHRgLfEF3Tg7hSMQt9tZ30ljbQUezPTVoQVmAqkVTa2pQYwyx7gSxqF3g0h/yMvfgUspmRygsD+bd8xxFPK4B5mTcrwb6xFxjTBtObwynrsVW5xLa375joaO5CSuZHOvDKqVU1oaTpv40cDjwnDHmRGdoxjfHp1lpBvi7iBjgv5yM8IC0O5tSaoobjxj8PWPM1wBE5FPA14FrMjcQkdnYvdwONsZ0i8hvgIuwe8cplROMMbQ3Rdm1tZX6d1pJxJK4vS5CRb7cG8JhGdoau2ms7WTvri6MZQiEPcw+qJiy2ZG86EGQDStpEe1MkEzYQzcKK4LMW1lE6czwVJhpZKTx+EVgkYjMB2qxY+mHMjcQkWKgyxgTw04MP2GMaROR/e47Fh7+2Q+oeeM1ApEIwYIiQoVFBAsLCRUWESgoHFGhQKWUGo7hRJmoMSbqjHnxO4V7Dhq3ltmOMcbUiUgl8IiIbDTGPDHQhtqdTSk1xY15DHZ+qUsJM0h3YezPiqCIxLF/xRvzX+2UGonujhgN29up3biX7o4YkpqFoyD3hnCkpgZtruu0a2B4XVTMiVBWHSGc/1/YgX2HbpTPKbBn3agM4ZsiiRfHiOKxMSYhItcC/4s9cvxOY8zrInKNs/42YCnwKxFJYhfAvHKofcf6ia1537lEOzqwkgm621pp2VXXWzgP8IcjhAoLCRamkhdFBDVxoZQaQ8OJJjVOVvcP2MmCvYzzSWqqq7ExpkFEHsSufjxggkIppaa4cYnBInITcCnQCpzYf70xplZEvg/sALqBvxtj/j7IsbQnmxp38Z4kTbXt1L7VQktDFwgEgh4KSnOvNEu8J0lTXQdNNZ10tcUQgaLKIOXVEYoqQrjc+Z2U6D/rhi/oYc7SUsqrIxRWBHG782voxjCMOB4bYx4GHu637LaM288Ci7Ldd6wtOOwI3n7+GYoqKwGwLItoRzvdba10tbU516207NplzyDg8IfDTo+LzORFIW5P7iULlVK5bThFMs9zbt4gIo8BRcDfxqVVgIiEAZcxpt25fRrwrfF6PKWUymUjjcEi8igwc4BV1xtj/miMuR64XkS+DFwLfKPf/iXYdSrmAy3Ab0Xkw8aY/xmgjdqTTY2LVF2Junda2LOjHWMMXr+bSEnu1ZWwkhYtDd001nTQuseeGjRU5GOuMzWoN3emyhwRyzL0dCVI9FiAIeLMulFWFSZcnHvvx3iY6HPiyeRyuQg5CYeyjOXpxEV7G92tdtKiu72V1oZdGCsjcREK270snGEiwcIiQgWFuL2auFBKDWy/CQoRKR1g8WvOdQR7Fo6h9r8XOAEoF5Ea7JNfL9gZYxGZCawFCgFLRD6DPQd0OfCg80HnAX5tjJmSwV8ppQYz2hhsjDkly4f6NfAX+iUogFOArcaYPU57fg+8C9gnQaHUWDKWoa2pm11b2qjf3Eoybk8NmpN1JVJTg9Z20FzXRTJh4fW7mbmgkPLZEYIF+T01aDJhEe2MYyUNIkJpVZgZ8wspmRnOyRlRxsto4/FUkpm4YHZv7U5jWUQ7O+yERUavi/6JC18oRKjASVg4vS6ChYV4vPn9t6KUGr1selCswx6XnHk2kLpvgAVD7WyMuXg/63dhVyLurw1YmUX7lFJqKhtVDB6KiCwyxrzj3D0b2DjAZjuAo0QkhD3E42TspLJSY84YQ1drjN3b26h9ay+x7kS6roQ7B+tK9HTFaaq1Z+Ho6bSnBi2eYU8NWlie31OD9taTMHi8bmbML6LygEKKK4N48rwXyCiMWzyeKsTlIlhQSLCgcIDERSfd7a10tdq9LbraWmnd3ICxemcN8QVD+/S2CBYW4fFp4kKp6WK/CQpjzPyJaIhSSql9jXMMvtkp7GYB23Fm8BCRKuAOY8x7jTHPi8jvgJeABPAyzjAOpcZKtCPOnp1t1L7VQkdLD+KCQNibk3UlknGL5l2dNNV00J6aGrQ0QNXCIkpmhfNuyswUYwzxaJIep56EP+Rl7iFllM+2ky2uqVtPImt6TjxyduKigGBBAaVVvb9LGmPR09np9LhoS/e82L1lU5/pTr2BYJ8ZRVJ1LjRxodTUk80Qj/RYYxE5xhjzdMa6a40xPxvPBiql1HQ2njHYGHPBIMvrgPdm3P8G+w79UGpUYtEETbUd1L7VQuueLgwQCHkoKM29OgbGGNoaozTWdNCyqwvLMvjDHmYvdqYGDeXnDAbG2PUk4tEkCERK/Mw5pJSyqgLCxVNjZpGxpOfEY0/ERSBSQCBSAFW9y40x9HR2poty2jUu2mjYunmAxEVGYU6nx4XX75+EZ6OUGgvZfKJ+jt6xxj8FVmesuwLQYKyUUuNHY7CaMhKxJM31ndS900pTbTvGgC+Qm8UuAbrb7alBm2qdqUE9LsqqI5RX529BSCtp6OmKk4hbIEJxZZBZC4sonRUhEMm9YTQ5RuPxBBERApEIgUiEkqrZ6eV2Uq03cdHd1kZ3WxsN27ZgJRLp7bz+QJ/eFqkEhiYulMp92SQoZJDbA91XSik1tjQGq7yWTFi07O6iblMLe7a3YxmD1+siUuxHcqzYJdhTgzbX2XUlulrtqUELK+ypQYsr83Nq0D5FLl1CeXWEmQuKKJ4RwhfIz94fk0Tj8SQTEQLhCIFwhJJZfRMXse4uu75FWytd7a10t7axZ/tWkhmJC4/f32eISLCgkFBREV5/7g0nU2q6yuZTyQxye6D7SimlxpbGYJV3kkmL1oZudm9tZdeWNqykZc/AUejLyS/4VtLQ0tBFkzM1qDEQKvQx5+BSyqrCeP35VxQyEbeTEsYyuD0uZswvYsa8Qooqg3i8+fd8coTG4xwlIvhDYfyhMCWzeseKpBMX6d4Wds+Lxu3bSCbi6e08Pv8+9S2ChYV4/fld7FapfJRNgmKJiLyKnRle6NzGuT/tqxUrpdQ40xis8oJlGdr2dLN7W++0oC63EIh4cedggUVjDJ0tPTTWdNJc30kybk8NOmN+IWXVEUJ5ODVoIpYk2pnAYPD63FQfVELF3AIKK4I5+R7kIY3HeaZP4mJm/8RFN91tqRlF7ORF487tJOOZiQtf394WTgLDG9DEhVLjJZsExdJxb8UkM7EYxmjiWymVk6Z8DFb5y7IM7U1OUmJTK4lY0q7WH/Hk5LSgAD3ddnHOppoOop32NKYlM0OUzw5TWB7MyWEngzHGkIhZRLviYOyZNw5YXkZ5dQEFZQFcefRc8oTG4ynCTlyE8IdCFM+clV5uz2bTnU5YpHpeNO7cQTIeS2/n9vr6Fud0el94A0FNXCg1StlMM7p9IhoymXb8y7/Q9fwLSCCAy++3rwMBxO9PXw+2TLxeDURKqXEzHWKwyi/GMrQ1Rdmzo426TS3Eo0nEJQTCXoI5WmQxmbDYu6uLxpoO2puiABSU+pm5sIjSmWHc3vzpXWCMId6TpKcrkR6KsnBVJWXVkZwtNjpVaDye+kQEXzCELxiieMbM9HL77y7qJC16kxfNtTtp2Lo5vZ3b680YJlJIsMC+7Qtq4kKpbGllJKDo7HNINDYhHg+mpwfT00OyrQ0TjWL19EBGcZ19uFy9SYuMBMdAyzSxoZRSKh8Zy9DeHKVhRzv177QQiyacpISHQCg3kxLG2ImUppoO9u7qwkoa/CEPVYuLKZ8dxp+j7R6IMYZYNEmsOwEGwsV+5qwpo3x2hFCRTgeq1HgTEXyBIL5AkKLKbBMXGT0uPF6C6R4XvT0vfMGQ/v0q1Y8mKIDi886l8/nn8VZVDRgkTCKB6enBikbTSQsTjdr3nYRGal2yvR3T2IgVjQ6d2BAZVkIjvZ1PT0SUUkqNvz5JiU0txLoTztR/HgpCuVvxvrs9RmNtJ021HcSjSdweoWx2mLLZ+dXDwB4jn6QnmkAMREoDzFteRtnsAoIF+iOHUrlgsMQFQLwnas8q0t7mDBVppaW+lj3btqS3cXk8hAqK9inQ6Qtp4kJNX8NKUIhIEJhrjHlrnNqTk8TjQTweXOHwsPYzyeS+CY3U/X5JDquzk2RzMyYaxWQU59m3MWInLbJMaPRJbLjypwurUmpf0zUGq4mTHr6xM6OnhNg9JQpKczcpEY/ZU4M21XTQ2RoDgaLyIOVLIxTPCOLKkwKRdlIiQSyaxBgoLAswf1UFZbPDBCP5V7RzKtN4rPbH6w9QVBmgqHJGn+Xxnp6M+hZ2z4uWXfXs2b41vY3L7emXtLB7XfhDYU1cqCkv6wSFiJwFfB/wAfNFZBXwLWPM2ePUtrwnbjcSDo8ssZHqlZHRO2PAZV1dxPfutW/HYkMed9DkxWC1NzSxoVTO0BisxotlGdoau9mzo536zS3Ee5K9SYkc7ilhWYbWhi4aazppbejCGAgWepmztMSeGjSQH51EM5MSAAXlQRYcWkxZVYRAjtb0mO40HqvR8Pr9eCsqKayo7LM83tNDd3tbn+RFy+7+iQv3PvUtQoWF+MMRTVyoKWM4n943AEcAjwMYY9aLyLyxb5IStxsJhXCFQsPaz1jWvj00+vfUSA1LiUaJt7Skh6gM2Z5BemxkFgwdqJioJjaUGlM3oDFYjREradHWGKVhuz0laCKWBBGCkdytKQHO1KCtMZpqOmiu6yThTA1aOa+Q8uoIocL86GUwaFJidoRAOHdff5V2AxqP1Rjz+v14/RUUllf0WR6P9dDt1LfobrOHi7Q27GbP9m3pbVxuN8GCvjUugoVFBMJhRPR8XOWX4SQoEsaYVs3O5S5xuUae2OhXS8Pql+DIvJ11YsPn22+djQGTHG73aF4GpaYqjcFqVJIJi9aGbhq2t7F7axuJeO7PvpHSOzVoJ9HOOOKCkhkhyqojFOXJ1KCZhS6NgcIKTUrkMY3HasJ4fX685fsmLhLxWDphkep10bangcYd29LbiMtNsLDAqXNRlB4yEoho4kLlruEkKDaIyIcAt4gsAj4FPDM+zVITSVwuJBiEYJDhpAaMZWFisb49NFIJjYGSHe3txJ11GDN4e7zeYRUOTSc7NLGhpjaNwWrYEvEkLbu7adjWxu5tbVhJC5dLCES8BAty+0txamrQppoO2pypQSMlfuYtKKNkVgiPN/djvjGGeDRJT7ddNLugNMD8FeWUVUe0pkR+03isJp3H66OgrJyCsvI+yxPxuNPbIjVUpI22pj007uydJVdcrn16XIQKiwiEI9oDWk264SQoPglcD/QAvwb+F/j2eDRK5QdxuZBAAAKB4SU2jOlNbAzQQ6P/cBSrsTGrxAYez+AJjcxkR//hKJrYUPlBY7DKSrwnScvuLnZtaWXPznZM0uDy2EkJd44XizTG0N4UpTFzatCgh6pFRXnT08AYQ6InSbTL7ikRKfFzwPIyyqsLCBZoUmKKGHE8FpHTgVsAN3CHMebmfuuLgP8B5mKfp3/fGHOXs24b0A4ksXtxrBmLJ6OmFo/XO2DiIhmP09WnxkUbHU2NNO2TuCjIqG9hzzASiBTg0sSFmiBZJyiMMV3Ywfj68WuOmg7EmYkEv3/EiY3M3hqDJjaam3sTG5Y1+IFTiY3M4qCD1dfITGx48qMAm5oaNAarofR0xWmu72TXllaa6zvBArfPRajAh8ud+93QuzviNNV00FTbQcyZGrS0Kkx5df5MDRqPJenpjNvFOgt8HLhmRrouRj60X2VvpPFYRNzArcCpQA3woog8ZIx5I2OzfwXeMMacJSIVwFsico8xJlUJ/URjTOPon4WabtxeLwWlZRSUlvVZnkzEM4aK2Ncde5toqtmR3kbERaCgoE9vC3uoSASXS3/oU2NrOLN4PAJ8wBjT4twvAe4zxrxnnNqmVB+ZiY3hMMZg4vFBh570nx3Fam5OrxsyseF2D5nQGKhwqCsQAI9HT1bVsGkMVpmMMXS320mJ+s0ttO3pBsDjcxMp8udFTYZELElzfSeNNR10ttjfvYoqglQvDVMyI5QXU4Mm4kmiHQmMMfhDXuavrKBibgHh4vxIqqiRGUU8PgLYZIzZ4ux3H3AOkJmgMECB2P+AIkAzkBjbZ6BUL7fHS6S0jMg+iYtEv1lF2ujYu5emmp3pbUSEgNPjInNK1EBBgSYu1IgN5yfg8lQgBjDG7BWRyiG2VyoniAji84FveF1rjTEQjw/aU6NPsiMaJekUD7WiUUgmBz+w2531TCiZQ1bwevWEd3rTGDzNGWPo2NtDU20Huza30tnagwH8AXfe9DKwLEPrnm6aajpo2e1MDVpgTw1aWhXGlwdTgyYTFt0dCYxlzyAy95BSKg8opKA0kBeJITUmRhqPZwM7M+7XAEf22+ZnwENAHVAAXGiMSf1aYoC/i4gB/ssYc/tADyIiVwNXA8ydOzeLZim1L7fHQ6SklEhJaZ/lyUSCaHubPVyktZWu9la6WvfSXJvxT1uEYKSAoDMdaqioyK55UVCIS4dWq/0YzpmAJSJzjTE7AETkAOxAqdSUJCLg8+H2+aCgIOv9jDGQSOw7E8pAhUOjUZJtbenbQyY2XK6hZ0IZpJioaGJjqtAYPA2lpgNtrGmnfnMrMafYoj/kyZukhDGGrrYYjampQWMWHp/Lnhp0dphQ0fB6xU0GK2mIdsZJJizcHhdVi4uZOa+QwoogLk1KTEcjjccD/WPpv997gPXAScBC4BERedIY0wYcY4ypc5Ihj4jIRmPME/sc0E5c3A6wZs0a/ZxQY8rt8RAuKSXcP3GRTBBtb0/3tuhua6WrtZXm2lp6/5kLgUikT32LUGGRJi5UH8NJUFwPPCUi/+fcPw4nOzsUEbkTOBNoMMYsG2D9EuAuYDVwvTHm+xnrhiwkpFQuEhHwenF7vRCJDGtfk0gM2kPD9Et4JNvbMY2NdmIjMUTvz8zExiAzoAy0Tnw6bjrHjCgGq/yTiCfT04E2bGvvnQ405KGgNDDZzctaLJqgqdYewhHtsKcGLZ4Ronx2JC++2BvLEO2Kk+ixEJdQOa+QWQuLKJ4Zyvlio2rcjTQe1wBzMu5XY/eUyHQ5cLMxxgCbRGQrsAR4wRhTB2CMaRCRB7GHjOyToFBqMrjdHsLFJYSLS/ost5JJujvaM4aK2AmMvfW1GcXvU4mLwoyhIkUECgpwu3O/Z50aW8Mpkvk3EVkNHIWdAf5slkV67sburvarQdY3Y0/PdG7mwiwLCSk1pYjHgzsSGVliY5CaGqZ/EdGODpJNTfa28fgQjZHhFw51ZkzRxMbYG0UMVnmgpzvB3l2d7N7SRlNtB8ZyZt4I5/50oJmSSYuWXV001nTQ1mhPDRou9nPAsjJKq3J/alBjDLHuBD3RJAKUzY5QtaiY0llhPL7cbruaOKOIxy8Ci0RkPlALXAR8qN82O4CTgSdFZAZwELBFRMKAyxjT7tw+DfjW2DwjpcaPy+0mXFRMuKi4z/LMxEVvkc5WWurr7N7IDn840re3hdPjwq3F6qes/b6zIrLEGLPRCcTQm+md63Rve2mo/Y0xT4jIvCHWNwANIvK+fquyKSSklMJJbHg8EA4Paz+TTA5dODRzOEpnJ8nmZjvhEYsNflAnsbFPQmOgYSmZCRCfT+feHsBoY7DKTalhD831neza3Epbo1Pk0usiVOTL+d4FmYwxtDf30FTTQfOuTqyEwRf0UHVgEWXV+TE1aLwnSbQzDgYKygIsXF1JWXUB/qCeAKteY3BOnBCRa7GnJXUDdxpjXheRa5z1twE3AneLyGvYyY8vGWMaRWQB8KDzA4AH+LUx5m9j/iSVmiCDJi6sJNH2Drrb7SEi3e2tdLW10bKrnt5yLOAPh53hIZnJi0Lcntz/zFFDy+aT93PY3dZ+MMA6gz1GbjxkU0goTQsCKTV84nYjoRCuUGhY+5lkEhOLDdxDo3+yIxol7hQQNT09Q7cnM7GRbZ0Nv3+qJzYmKwarMWZZhvYmu0Bk/ZY2oh1xwOAL5E89iUzRzjiNqalBu5O4PELpzDBl1REKSnP/+djFLuOYpCEQ8XLgYZVUzC0kVDi8gspqWhl1PDbGPAw83G/ZbRm367B7R/TfbwuwcpjtVSrvuFxuQkV2Yc2y6t7llmUR7Wjv09uiq62Vlt27MBmz7vlD4fRsIpm1LjRxkT/2m6AwxlztXJ84/s3pI5tCQr0rtCCQUhNG3G4kGMQVDA5rP2NZ+w496T87SsayrBMbPt+gtTSGrLORB4mNSYzBagzEY3Y9iT07MupJiDhFLvOvzksinqS5rsuZGtT+uywsD1B9UEle1GawLEO0I04ibuH1ualeUsLM+UUUlAXy7r1QE0/jsVKTx+VyEXISDmUZpVyMZRHt7OiTtOhua6W1YaDERb9ZRQqL8Hg1cZFrsu67KCKXDrTcGDNYbYnRyqaQkFIqj4jLNfLERiy2b0JjoF4bPT32zCipxIYZPF8pPt+wEhqpZZNhEmKwGqFoR5zmXZ3s3trK3voujDG43PlXTyLFsgxte7pprOmgpaELY0Ew4qV6SQlls3N/alBjDD1dCeI9dnKo4oACqg4szouEispNGo+Vyh3icqWnMGX2vomLVI+LrjZ7uEhrQwPG6p01zxcM7VOcM1hYiMervekmy3DOKg7PuB3ALuDzEoMXvxytbAoJKaWmAXG5kEAAAgGGU6bOGNOb2MhiOIrV0UHcuT1UYgOPB09lBQUnTejoiomOwSpLxjK0N0fT9SQ6W+2eBV6fO+/qSaSkamQ01XbSVNuRnhq0Ym4B5dURQoW53/sjs65EUWWI6iXFlM0uwOvXYpdq1DQeK5XjMhMXpbN7x4oYYxHt7HSKc9r1LbrbWtm9ZRNWMjNxEbR7W/Qr0OnxaeJivA1nFo9PZt4XkSLgv/e3n4jcC5wAlItIDfANwOsc8zYRmQmsBQqx55X+DHCwMaZtoEJC2bZXKaXEKdiJ34+7qCjr/dKJjYwkRmYPjURTE56ysnFs+YBtGlEMVuMjEUvSuqebPTva2b2tjUTMPqmxh27kfv2FwaSmBm2q7aC73ZkatDJEeXV+TA2aqithWYZg2K4rUXlAIcECPaFUY0fjsVL5S8RFMFJAMFIAVX0TFz2dXfsMFdm9tW/iwhsI9qtvYV9r4mLsjKZfZhewaH8bGWMu3s/6XdjDNwZat08hIaWUGm99EhuFhfusj+/ahbeqahJa1kdWMViNDWMM0c44e3d1sXtrG3vrO+2hGy5n6EYk/4ZupFhJi727umiq7aR1jz2bSLjYxwHLSvNiek1j7LoS8ZiF2+ti9uISZi4oorAsgOR4QkVNGRqPlcpzIi4CkQiBSASqZqeX28MEO+lubaWrvXdK1IatW7CSifR23kCgzzSoqdveSRoanM+GU4PiT/QWqXQBBwO/GY9GKaWU6ktj8MSzZ92I0lzXwa4trXS12dPr5vPQjRRjDB17e2is6WBvfSfJhMEXdDNroT01aK4nXIwx9hCOrgQClM2OMHtxCaWzwri9WldCjS+Nx0pNHyJCIBwhEI5QwgCJC2eIiN3joo2GbVuwEhmJC7/fTlr06XFRiNcfmIynkxeG04Pi+xm3E8B2Y0zNGLdHKaXUwMYtBovI54HvARXGmMYB1p8O3II93O4OY8zNY/G4uSgWTTizbrSzZ4c96wZAIM+HbqREO+M01XbQVNNJT3cCl1somRWifHYkL2aySA/hSBpCRT6WHDmT8jkFBMK5nVBRU46eEys1zfVJXMzq7VVrjCHW3ZVOWKSGijRu30oyI3Hh8fsJFRQRLCoiVNA7ZMTjz/9zjdEaTg2K/xvPhiillBrceMVgEZkDnArsGGS9G7jV2aYGeFFEHjLGvDEe7Zloxhi6WmPOrBtttDZ0AeB2C/48nXWjv0Q8yd56e2rQjr29U4NWLS6mZGYItye3exykhtfEe+whHNUH2UM48iGhoqYmPSdWSg3Gnko8jD8UpmRm/8RFd29vi/Y2ulpbady+jWQint7O4/P17W1RUEiwqAivf/p85u03QSEi7fR2Y+uvB9gMXG+M+cdYNkwppdSExOAfAV8E/jjI+iOATcaYLU577gPOAfI2QZGMW7Tu6aaxtp3dW9uIddu/aPgC7inRSwLsmUVaG7tpqulg7+5ujGUIhL1UH1RM2ewIvmBuTw0KGbNwAKVVYaoPKtUhHGpS6TmxUmqk7MRFCH8oRPHMWenlxhhi0W66W52kRarHxc7tJOO9iQu317fPjCKhwkK8geCUOG/JtN8zFGNMwWDrnF/WlgH3ONdKKaXG0HjGYBE5G6g1xrwyxIfbbGBnxv0a4MhBjnc1cDXA3Llzh9uccZP6Bb5lt13gsrm+E2MZxCUEQh4KSqfOONCuthiNNR001XWQ6LHweF1UzIlQVh0hXJT7U4NaSYvujgTJhEUw4uXANTOYcUAhgRyviaGmBz0nVkqNNRHBHwzhD+6buIhHo+neFqmeF821O2nYujm9ndvr7ZOwSNW78OVx4mJUP6EYY5LAKyLy0zFqj1JKqSxlE4NF5FFg5gCrrge+Apy2n4cZ6NNtwF8QjTG3A7cDrFmzZrBfGSdEMmnR3ugUuNzaRnd7DAN4fS5Chfld4LK/eDRBU10njbUddLfFEYGiyhDl1WGKKkM5/1ztbq8JYtEk4hJmLiiialExReVBnYVD5Q09J1ZKjSURwRcM4gsGKZ7RexpnF4mOOgmL/omLWHo7t9dLsKBonylRfcHcT1yMSR9PY8x/jcVxlFJKDd9QMdgYc8pAy0VkOTAf+4Qa7OmeXxKRI5zpn1NqgDkZ96uBulE3ehxEO+O0NnSze3srTTWdWEnL6VI5NQpcZrKSFnt320M4Whu7wdhTg849pJSyqtyfGhTsoTZdHTGMZdfEOHDNDMrnFODNg7YrNRg9J1ZKjScRwRcI4gsEKars+/tTusdFWytdTq+L5vpaEtu2pLdxezxOL4tCu0ink7zwhUI5c56U+4NQlVJKjTljzGtAZeq+iGwD1gwwi8eLwCIRmQ/UAhcBH5qodg7FSlq0N/ewt76TXVtb6dzbAwIer4tgxIPLPbVqFaSmBm2q6aDZmRrUG3Azc0ER5bPDBAt8k93E/TLGEO2IE49ZeP1uDlhWzsz5RYSLc3/4iVJKKZXLvIEARYEARZUz+iyP9/SkExepHhct9fXs2bY1vY3L4yFYkFnfwk5i+EPhCf98HlaCQkQOABYZYx4VkSDgMca0j0/TlFJKZZqoGCwiVdjTib7XGJMQkWuB/8WeZvROY8zrY/2Y2erpitO6p5uGbe001rSTTFgAdi+J0qnVSyKlpytOY20nTTUd9HQ5U4PODFFenR9TgwIkYkm6OzIKXi5xCl7m+AwiSg1Gz4mVUvnC6/dTVFFJUUVln+Xxnh47aZFRnLNldz17tmckLtwepzCnM1TEmRp1PBMXWScoRORj2MXPSoGF2N18bwNOHpeWKaWUShvvGGyMmZdxuw54b8b9h4GHx+Jxhiuzl8Tuba3paTLdHhf+sAf3FOslkZKIW+ytt+tKdDTbz7mgLEDVovyYGhTAsuzeEom4hT/kYeHqSmbMLyQYyf2eHkoNRc+JlVJTgdfvx1tRSWH/xEWsh+6M+hZ24mI3e7ZvS2/jcrsJFhbh9fmoOGDBmLZrOD0o/hV7urnnAYwx9vD+ewAAlhBJREFU74hI5dC7KKWUGiPTJgb3dMVpaehmz/YBeklMsVoSmYxlaGvsprG2k727upypQT3MdqYG9efB1KBgTw/a3RlHgMoDCpm9uITimblfrFOpYZg28VgpNf14fX685RUUllf0WZ6Ixfr0tuhqa6Vj714SsZ4xffzhnO30GGNiqRNDEfEw+FzQSimlxtaUj8EtDV1sfLaezpbp0Usipas9RlNNB021ncR7kri9LsqrI5RXR/KmNoNlGbrbYyQThkDEy+Ij7OlB/SGdHlRNSVM+HiulVH8en4+CsnIKysrTy9ob91A+d97YPs4wtv0/EfkKEBSRU4FPAH8a09YopZQazJSPwR3NUdobuymsyP0psEYr3pOkqc6uK9HVFnOmBg1SNjtCcWUIlzs/nn88miDalUBEqJxXYPeWqAzp9KBqqpvy8VgppSbLcBIU1wFXAq8B/4I9HvmO8WiUUkqpfUyLGCxumbLJCStp0dLQTWNNB6177KlBQ0U+5h5cSmlVGK8/P6bXzOwtEUz1lphXhC9PhqAoNQZGHI9F5HTgFuyiw3cYY27ut74I+B9gLvZ5+veNMXdls69SSk0FWZ9NGGMs4OfORSml1ATSGJyfjDF0tvTQWNNBc10XyYQ9vebMBYWUz47kxdSgKfGeJNHOuN1bYn4h1YtLKKoIam8JNe2MNB6LiBu4FTgVqAFeFJGHjDFvZGz2r8AbxpizRKQCeEtE7gGSWeyrlFJ5bzizeLzGvuPrWoG1wLeNMU1j2TCllFK9NAbnl56uOE219iwcPZ0JXC6hZFaIstkRCsvzY2pQ6DsTR6q3ROW8orwp2KnUeBhFPD4C2GSM2eIc5z7gHCAzyWCAArGDRARoBhLAkVnsq5RSeW84Zxh/xc7e/tq5f5Fz3QbcDZw1ds1SSinVj8bgHJdMWDTX23Ul2lNTg5YGmLWwiNJZ4byYGjQlHksS7YgD9kwc1Uu0toRSGUYaj2cDOzPu12AnHjL9DHgIqAMKgAuNMZaIZLMvACJyNfY0qMydO3f/z2YfBruTCMMr/Snp/6XaMYLHVkpNd8NJUBxjjDkm4/5rIvK0MeYYEfnwWDdMKaVUHxqDc5AxhrbGKI01HbTs6sKyDP6wh9mLiymbHc6rWSyMsXtLxGMW/pCHAw+rZMb8IgLh/HkOSk2Qkcbjgb6x908BvAdYD5wELAQeEZEns9zXXmjM7cDtAGvWrBn27CLhklI6mpxOIKkkw1DJBmP6XBtjMKb/w0rvIUScpktqDUjvffsgDPiM7cOa9DYGA8YgYtcvEpfLToy4XLjEhbhSy1z2tXPfJS6QqVvzSKl8NpwERUREjjTGPA8gIkdgdz0Du+uZUkqp8aMxOId0t8dozJwa1OOirDpCeXWYcLE/r056E3GL7vY4xhjKqyPMWVpKyawwLu0todRgRhqPa4A5GfersXtKZLocuNnY3/A3ichWYEmW+46aiHDchy4bk2MZYycP7CQCGGNhnGucJIaxDGBfW1YSjMGyLHtbK3VxlllJ59rCSibT15aVJJmIk4wnSMZjJBMJEvEYyXicRDxOMn2JZdyPYVmWHatFSCVQ0nkRY+y2Yic/XC4X4nbjcrlxuV240rfdiCt/escplQ+Gk6C4CrhTRCLYf7ttwFUiEgb+33g0TimlVJrG4EkW70nSXGfXlehqjYFAcUWQsuoIxZVBXO78OUk1xtDTlSAWTeL1u5m/soxZC4vzqminUpNopPH4RWCRiMwHarGHhnyo3zY7gJOBJ0VkBnAQsAVoyWLfnJL68t+b6sytmYrsxEYCK5EgmUjYiYv0tXOJxYn1RIn3RIl1dxHvjhLr6SYejRLv6SEe7cZYVkZvDHF6j9jJGURwu924PB47qeH24PZ4ent6KKX2MZxZPF4EljvTH4kxpiVj9W/GumFKKaV6aQyeHFbS0NLQRVNtB60N3RgDoUIfcw4uoawqkjdTg6Ykk3ZvCStpKK4MsfRdZZRW5Vd9DKUm20jjsTEmISLXAv+L/W39TmPM6yJyjbP+NuBG4G6nEKcAXzLGNAIMtO/YP7vpw+Vy4/K5wecf8TGMMVjJJIlYjESsx7m2b8djPcS6uujp6iTa2UFPVyexri6629tJxGL2cBMnSZHqbSIuF26PB5fHg9vjtW+78+tzRqnRGs4sHn7gAmAe4Mn4g/rWfva7EzgTaDDGLBtgvWDP6fxeoAu4zBjzkrNuG9COXYgoYYxZk217lVJqKhlpDFbDZ08NGqOptoOmuk6ScXtq0BnzCymb/f/Zu+84Oer68eOv98zW67lceiEBQk8IEIqoVFFABLEjloDCDxWxoaJYsH6xl6985QuKICJYUYroFwVERZSACUWKJARITy7J9dsy8/79MbN7e3d7l72yd3t37+eDYXdnZmfeu3d53+x7P6WGqrqJ1cpAVcl0e6S6gtlE5u0/jXlLGiZcVxRjKsVI8rGq/g74XZ91Vxfc3wS8stTnmvElIriRoFVEvKqq5Of5nhe0yujuItOdIpPqJtPdRVd7G91tbXS1tdLd0U5nayt+NtPTFSXsGiOuSyQSxY1GcKNRHDdi+dxMGkPp4vFbgimUHgZSQ3je9QQjEv94gO2nAUvC5Wjg+/QelfjEXOXYGGOmsOHmYFOiVFc2KEpsaKe7I4s4wrTZVTTNr6FuemLCzWDh+0pXWxovq1TXxznwsJnM2KuWaMy+jTNmhCwfmxFxXJd4VTXxqupB91NVvEyGdHcX6a4u0l2dpLs66WzZTWdrK50tu+lqayXT3RUUMMIiBqpB4SQWIxKN4UYjiFhLOTMxDKVAMV9VTx3qCVT1fhFZNMguZwE/DgcDelBEGkRkjqpuHuq5jDFmEhtWDjaD87I+u7Z0smNDO23N3QDUNMZZtHc4NWh04l3QZdMeXR0ZUJi1uI75+zdSPzNp364ZM3osH5sxISJEYjEisRhVdfUD7udls/kuJKnODrrb2+jYvStYWnbTWTgrix/MghKJRYMCRiyG6w7lI6Ex5TWU38YHRGSpqj42yjEUm9d5HrCZYDDd/xMRBf43nDapqJHP+WyMMRWtXDl4ylFV2pqDqUF3benE95R4VYS5SxqYPr+axASaGjRHVUl1ZEmnPGIJl30Om8nsvW2KUGPKxPKxqShuJEJVXf2ARQzf90h1dpLq6CDV0U5nawttzTto39lMx+6deJlMz3gYQCQaJRKLE4nFbAwMM+aGUqB4GbAynO4oRTBwj6rqshHGMNi8zi9V1U0iMpNgHuinVPX+YgcZ6ZzPxhhT4cqVg6eMrrY0OzZ20LyxnUy3hxsRps+rZvq8GmqmTczxGHzPpzM36OWsKg48eDqN86pxJ9CMIsZMQJaPzYTiOC7JmlqSNbX9tqkq2VSKrvZWutvb6WzdTev27bQ1b6dj165g+lcR8H3EcYjG40TicdxIdEL+3TSVbygFitPKFMOA8zqHAwWhqttE5FbgKKBogcIYYya5cuXgSS2b9mje1EHzhnY6wqlB65uSTD+whmmzJtbUoIUy3Vm6OzzEhXn7TWPefjbopTFjyPKxmTREhGgiQTSRoK5pZq9twZTUHfmBO9t27qB1+zbadmync/duRIJpVUWESDxBNGGFCzNyQ5lm9HmAsDVDYhRjuA24WERuIRgcs0VVN4dzSTuq2hbefyVgo9UbY6akMubgScf3lZZtnezY0EHLtk5UIVkXZcGB05g+t5poYmL2tVVVutozeGmfRE2U/V8ym1mL6ibcVKfGTHSWj81UISIkqmtIVNfQMHsOc9g/vy2TTtHV2kpXawttO5tp2bqF1u1bw8KFE8424hCLB8UP6ypiSjWUaUbPBL4BzAW2AXsBTwIH7+F5NwMnAE0isgH4LBCF/LRKvyOYYvRZgmlGzwufOgu4NazARYCfqurvS43XGGMmk+Hm4KlCVeloSdO8oZ2dmzrIZnwicYeZi+pomj/xpgYt5GV9utrSqA/T59ew8KDpTJtdNeFmFTFmsrB8bAxEY3GiTTOoa5rBrL33za/PpLrpbGkJu4psY/eWzbRs34bvZQl6Q/lEYnFiiSRu1FpbmP6G8jXSF4BjgD+q6mEiciJwzp6epKqD7hPO3vG+IuvXAYcOIT5jjJnMhpWDJ7t0ODXojo0ddLdnEAemzapi+vwa6puSE/pDfLorS6ori+M6LDx4OnOXTJvQhRZjJhHLx8YMIBpPUD8zQf3MWczZN2hxoap0t7fR2bKbtp3N7N6yiV1bNtPZsjs/NaobjRJLJonErLviVDeUAkVGVZtFxBERR1XvFZGvlC0yY4wxhSwHh3JTgzZvbKd1Rzg16LQ4i5ZOZ9qcKiLRiduMVFXpasuQzfhU18c58LCZzNyrlkhs4r4mYyYhy8fGDIGIkKytI1lbx/T5C2HZYQBkurvp2L2L9t072bVpI7u2bKJtx3bEcVBfcaMRYskqIrGYFS2mkKEUKHaLSA3BIJU3icg2IFuesIwxxvQxpXNwfmrQje3s2hxODZqMMHdJPdPn1Uz46TRz3Th8H2YsrGXhQY00zLRuHMZUqCmdj40ZLdFEgobZc2iYPYf5BwQ9pLLpNB27d9K2s5ldmzeya9NG2nZsB3FAlUg8FhQtohP7774Z2FAKFGcBXcCHgHOBemzQSmOMGStTMgd3t2fYsbGd5o3tpLs8nIjQOLeapvkTd2rQQunuLKnOsBvHIU3M3bfBunEYU/mmZD42ZixEYjHqZ86mfubsfNEik+qmfddO2nbsoHnjC+zctIGO3bvCawAhnkwSTSZxnIk5M5fpraQChYi4wG9V9RWAD9xQ1qjMpKKqoAq+j/o++H7vx7ntqv0ehwdAw9uiRJDwNve4cBERcJzgseP0PHYcpPDWmAo11XJwNu2xc3MHOza007E7DUD9jCTzD6imYVYV7gSdGjSnsBtHVV2MA4+da904jJkgplo+NqYSROMJps2ey7TZc1l4yLJg+tOODtp27qBl6xZ2vPg8Ldu2oqqo+kRiMeLJKhuEc4IqqUChqp6IdIpIvaq2lDsoUznU99FsFrJZ1POC+7lb3wcUKCgIQO9Cgmqwj+Mi0QgSjSHRKBKLIm4EiUUhEkUibvA44kIkEhQNXDdfQEDCwoLQc57c8QsKG+r5QXzqQ9ZDsxnU8yCTDe7nbtMZtLsbP5NG0xlQP3gd+ebUuVsNXmLuNTkOEokgkQiEtxKJgOtaAjRlMxVysOf5bF7bwsZndtOxO4X6kKwNpgZtnFtNbIJODVooPxuHQtMC68ZhzEQ0FfKxMZVOREjU1JCoqWHGwkXse+QxeNls0DVkxw6aN7xA84YX6Gxtye8fr6oimkja9foEMJQrvm7gMRG5G+jIrVTVS0Y9KlMWqhoUGrJZNJMJlvCx5Fsc5HcGXyHi4iSrcKqrcKuqcKqqcaoLlmQiKDrEYkHRobAAEQ2XSASp4LmPVTUoauTej3Q6WDIZNJXCT6fRVBq/uwu/vQO/vR2/ox2/vR2vowOvtQXt7qZXgaOgqJF/H6JRJBYLChuWHM3QTeoc/OITO/nn7c/hRoSZe9UxfV41VXWTY1CsTHeWro4sbkRYcNB05u1ns3EYM8FN6nxszETkRiLUNc2krmkm8w44KJg5pKOdth3b2bV5E9uffy4cyyK4roglk8SSVdYtpAINpUBxZ7iYCqO+31NwyGSCD9e5okP+H11QcJBEAre2FrdxGm5dHW5dHU59PW5VFZJIBgWHRAInmcSJx2EKNI0SkXxriOFSz8Pv6ka7u/C7uvA7O4OlrY3srt14u3fh7W7Bb9mN39kVFDLCwX5QP2hhEo/jxGJIPG4tMkwxkzoHLzi4kaPPWsyuzR3Uz6ga73BGLJhSLUMm7ZOsiXLAsXOYtaiOqHXjMGYymNT52JjJQERI1tSSrKll5qK92f8lLyOTTtG2Yzu7t25m+/rn2L11S9DqmmDAzniyCqeCv1SdKobyiexnwL4E3w2vVdXu8oRkCqnv93yjn1vQnjETfAURnNoaIvX1uNOm4U6bhjNtGm51DU5V0PrBqarCSSZH9CHcDExcF7emGmqq97ivZjL4HR147R35lhjZ5may27fj7dxJtrkZ7U6FRQxB/eDnLfE4TjyOxOMV3SLFlM2kzsGu6zB7cT27t3aOdygj4ns+nW0ZfE9pnFPNXodMZ9qcahzrxmHMZDKp87Exk1U0Fqdx7nwa585n78OOxMtmaGtupmXbZratX8euTZtQ30MJxr2IV1nBYjzs8dOqiESALwPnA88DDjBfRH4EXK6qmfKGOLmp56GpFJpO46dS4GWDb9aFsIuA4NbXE5k5E7dxGtGmGbgN9Tg1NcFSXY1TVWWDPE4gEo3iNjTgNjQMuI+fSuG3tuK1teG3tpLZsYPslq1kt28ju2N7MP6HOOGtBK1eEomgeGG/C5PKWORgEbkU+BowQ1V39Nm2APgxMJtgQLhrVPU7Iz3nZJNNe3S1ZxAR5u7XwPz9G6mZFh/vsIwxo8iuiY2ZXNxIlIZZs2mYNZu9lh6G73m0Ne9g97aghcXOjRvwfQ8UYskEsaoqHMcKFuVWytfpXwNqgcWq2gYgInXA18PlA+ULb+LLt4Do7u5fgPA1+LDa2Eh03lwiTU1EZszAqanFravFqa214sMU5cTjODNmEJkxA4BkwTb1/WD8i5YWvN27ye7YQWbjJjJbtpDdujUobImA+sGYIMlkULyw1jMTVVlzcFiAOAV4YYBdssBHVPUREakFHhaRu1X13yM572SgqqS6sqS7PGLJCPuumMWcveuJJe3fmjGT1IjzsYicCnwHcIEfqOqVfbZ/lGDqUgiu0w8kKB7vFJH1QBvgAVlVXTEaL8oYE3Bcl/qZs6ifOYu9DlneU7DYuoltz61j56aNoD4KxKuqiCWTiNjntNFWylXUGcB+qj1TM6hqq4i8B3gKK1AErSC6u/G7u9F0OviA6DjB2AIiuNOmEV2wgOismURmzsSprcWtr8etrUWSNpqsGRpxnPz4ISxY0Gubel5QuNi1i+zOnWQ2biSzcRPZrVvQTDboNhIWxqSqKihcWNO1SlfuHPwt4GPAb4ttVNXNwObwfpuIPAnMA6ZsgUJ9pbMtjZ9VahoT7H/0HJoW1Ez46U+NMXs0onwcTlF6FUFReAPwkIjcVljwVdWvERRCEJHXAB9S1Z0Fhzmxb0s3Y0x59CpYLD0ML5uhdcd2dm3eyNZ1z9KybSsQzhJSXU00nrDPdaOglAKFFibigpWeiPRbP1n1KkKkUj3TXYYf9iIzmogtXkx09izcxum4DfXBAJS1tdYCwowZcV0ijY1EGhuJ77MPHHkkEHzT67e2km3eSXZnM5kXXySzYSOZLVt6plhFg4FSq6qCWVkswVaKsuVgETkT2Kiqa0r5eYvIIuAw4B8DbL8QuBBg4cKFIwmtInnZYHwJVJm1qI4FBzVS12RFZmOmkJHm46OAZ1V1HYCI3AKcxcAF33OAm4cbrDFmdLmRKNNmz2Xa7LnsfdiRZFLdtG7fRvOGF9i67llad2xHENxohER1DW40Ot4hT0ilFCj+LSLvUNUfF64UkbcRVIsnD9Wg739XVzBtZEFLCIlEiMyYQWyfvYnOno3b2EikoQGnvgGnusouUE1FEwnGMnHr64nvvRhWBK1C1fPCbiLNZLdsIf3C86RffBGvuTkswPnB4JzV1UjCqsLjZEQ5WET+SDB+RF+XA58EXllKECJSA/wK+KCqthbbR1WvAa4BWLFixaQpYBdOE7rXIdOZt18DyRqbJtSYKWik18TzgBcLHm8Aji62o4hUAacCFxesVuD/wmLI/4Y5t9hzJ3Wx2JhKEY0nmD5/IdPnL2S/Y15Gd3s7Ldu2sG39OratX0umZTeoEk0miVdV25SmJSqlQPE+4Ncicj7wMEFyPJKgW/zZZYxtTLk1NWS3bsFtbCS+eDHRefOIzGjKD2bo1NTYhzMz6YjrEpk+ncj06bD/fvn1fkdHMLbF9u2k1z9P+vnnyW7eHM4q4uPEYkHRwroojYUR5WBVfUWx9SKyFFgM5FpPzAceEZGjVHVLn32jBMWJm1T11yN4LRNGMH96lkzKs2lCjTE5I70mLvYHc6Bi7muAv/Xp3vFSVd0kIjOBu0XkKVW9v98BJ2mx2JhKl6ipIVGzL7P23hf1fdp372TXpk1sXfcfdm7agKoiIiSqa4jE43YNPYA9FihUdSNwtIicBBxMkFzvUtU/lTu4sTTjgx/EScRtIEFjAKe6mlh1NbG99qI6bG3hp1Jkt+8gu20b6fXPkXruuaBo4Ti9W1pYwh1V5crBqvoYMDP3OBx8bUWRWTwE+CHwpKp+cyTnnAh8X+lszaCeT/2sKhYtbaJxrk0TaowZlXy8ASgcPGo+sGmAfd9Cn+4dqropvN0mIrcSdBnpV6Awxow/cRxqG5uobWxi4SHLyKbTtGzbyo4Xn2fL2mdo27EDUCLxOInqGpvOtEDJn8ZV9R7gnjLGMq7cmurxDsGYiubE48TmzyM2fx5Vhx8GgN/dTXbbNjJbt5Jet470uufI7twZdA9Bcaqqg6KFFf5GbCxzsIjMJRhd/nTgpcDbgcdEZHW4yydV9XdjEctY8TI+nW1pAObs08D8A6dR22jdmowx/Y0gHz8ELBGRxcBGgiLEW/vuJCL1wPHA2wrWVQNOOFhxNUH3vM8PIwZjzDiIxGJMn7+A6fMXsN8xL6WrrZVdWzaxde1/2PHC88F0pgjJGmtdYZ8ajDHD5iQSxBYuJLZwIdXhgJxeezvZLVvIbNxI93+eJfPC82g2CwoSjQbdpWw8i4qjqosK7m8CTg/v/5XizZInhXR3llRnFjfisPjQJuYumUai2ga1MsaMPlXNisjFwB8Iphm9TlWfEJGLwu1Xh7ueDfyfqnYUPH0WcGv4tzMC/FRVfz920RtjRouIUFVXT1VdPfP2OxAvm6Fl2zZ2bHiezf95mrYdO1BVYskE8epqHGdqta6wAoUxZlS5NTW4++5LfN99qTn+eNTzgkE4t24htXYtqWefJbN5U37eaKe6Oiha2MBBZowUji9RVRvLjy8RiU6tCwBjzNgLW5/9rs+6q/s8vh64vs+6dcChZQ7PGDMO3EiUxrnzaJw7jyVHviRoXbFpI1vWPsOOF19AfR9xHZI1tURik3+QbitQGGPKSlyX6KyZRGfNJLlsGQBeewfZLZtJv/AiqaefJv1iOKi5+kiyCre21rqFmFHn+0pnWwbNBuNLLF7WxLQ5Nr6EMcYYYypDr9YVBxwUjl0RzAyy+dln6GxtAVXiVdXEqibnTJL2CcAYM+bcmup8K4vak05E0+lgHIsNG0g98wzptevQbBZVHyeeCAoWU6BibMrDy/p0tmYAZfbe9Sw4qNHGlzDGGGNMxQvGrgimMj3gpcfTvrOZ5o0vsvmZJ2nZvg1VJRqLk6iZPANtlr1AISLXAWcA21T1kCLbBfgOQX/nTmClqj4Sbjs13OYSDNh2ZbnjNcaMPYnFiC1YQGzBAmpe8pKwW8gO0hs3kn76Gbr/8wy6ozvYNx7Hqa3FsYKF2YNMd5aujiyRqMPiZdOZu5+NL2GMMcaYiUlEqJ3eRO30JhYtO4xUZwe7Nm1k07NPs+P59fi+hzgOydpaItGJe508Fi0orge+B/x4gO2nAUvC5Wjg+wRTOLnAVcApBNMyPSQit6nqv8sesTFmXAXdQmYRnTWL6sMPR30fr7k5GHjzmWdIPf00meYdwcCb8bi1sDB5qkqqM0u62yNRE+WAY+cwe1Edkdjk+FbBGGOMMQYgXlXN7H33Y/a++5HNZGjZuoVt69ey6T9P0dnSAkCiuoboBBucvuwFClW9X0QWDbLLWcCPVVWBB0WkQUTmAIuAZ8NBgRCRW8J9rUBhzBQjjkNkxgwiM2aQXL4cVcXbsSMoWDz9NKmnn8Zv7kZVcRJJ3Lo6G8NiilFf6WxL42WV+hlJDnxpE9Pn1dj4EsYYY4yZ9CLRaH4a0wOOPY625h1sf/F5Nj3972BWEJR4sop4dVV+oPpKVQlX8POAFwsebwjXFVt/9EAHEZELgQsBFi5cOPpRGmMqhoj0Llj4PtntO0hveJHUk0+R+s8zaCYDCk5VFU5tLTJJ+uWZ3jzPp6stje/BrEV1LDy4kbqm5IT6psAYY4wxZrSI41A3YyZ1M2ay92Er6GprpXnDC2x8+kl2b9kICpF4OG5FBU5hWgkFimJXkTrI+qJU9RrgGoAVK1YMuJ8xZvIRx8nPFFJ9xBHBGBZbt5J6/nlSTzxB6rn1oD4ATm0dTnW1fYCd4LJpj672DOII8w9oZP7+jVTVWTcfY4wxxpic/KwgBy1lwUFLSXV2smvTBjb95ym2P78e9X3ciEuitg63QlofV0IUG4AFBY/nA5uA2ADrjTFmUOK6ROfOJTp3bjDoZjpNZtMmUs89R9djj5PdvDnYMRLBravDSSTGN2BTElUl3e2R6swSS7gsOXIWs/euJ5aohD9lxhhjjDGVLV5V1TNuRTrNrs0b2bL2P2x59hk8LxsOsllHJDp+g4pXwlXdbcDF4RgTRwMtqrpZRLYDS0RkMbAReAvw1nGM0xgzQUksRmzRImKLFlF74ol47R1kNm4Ixq94/AkyO3cG+yXD8SusO0hFUVW62zNk0j7V9XGWHD+LGQtqcSOV3YfSGGOMMaZSRWIxZuy1mBl7Leag405i99bNbHtuLZueeZLOlt2ICInaWqKx+NjGVe4TiMjNwAlAk4hsAD4LRAFU9WrgdwRTjD5LMM3oeeG2rIhcDPyBYJrR61T1iXLHa4yZ/Nyaatz99yex//7oa16D19xM6oUX6H7scdLP/gfNeiDg1NXjVFVZd5Bx4vtKZ2sG3/NpnFPNomVNTJtVhdjAl8YYY4wxo8aNRJg+bwHT5y1g/2NfTuu2bWxbv5aNT/2b1pYWECFZU0skHi/7dfFYzOJxzh62K/C+Abb9jqCAYYwxZSEiRJqaiDQ1BVOaZjJBd5C16+h6dA3ZzZtRFCcWx62vR8axydtU4WV9OlszgDJnnwYWHNRIbaN1wzHGGGOMKTfHcWmYPYeG2XNYctSxtDXvYNvz69j45BO07dgOQKKmlmiZukhXQhcPY4ypGBKNEttrL2J77UXtSSfitbaSfuFFuv/9BN3/fhJNp/BaW8c7zEkr1ZnFjTjstXQ68/ebRqLGCkLGGGOMMeOhcEaQfY44mvZdzWx//jk2PPkEbTt2kOpsH/VzWoHCGGMG4dbVkTzkYJKHHIx6HpnNW0ivXUt0wYI9P9kMSV1TkkNPWsCsxfVE4zYOiDHGGGNMpRARahubqG1sYvHyFXTs3sX255+jfubsUT2PFSiMMaZE4rrE5s8jNn/eeIcyKdU1JalrSo53GMYYY4wxZhAiQs20RmqmNY76sW0IdGOMMcYYY4wxxow7K1AYY4wxxhhjjDFm3FmBwhhjjDHGGGOMMePOChTGGGOMMcYYY4wZd1agMMYYY4wxxhhjzLgTVR3vGEadiGwHnh/i05qAHWUIZ6gqIY5KiAEqI45KiAEqI45KiAEmdhx7qeqMcgRTaSwPj4pKiKMSYoDKiKMSYoDKiKMSYgDLw3tkuXhSxACVEUclxACVEUclxAATO44Bc/GkLFAMh4isUtUVFkdlxFApcVRCDJUSRyXEYHFMbpXynloclRVDpcRRCTFUShyVEEMlxTHZVMr7WglxVEIMlRJHJcRQKXFUQgyTOQ7r4mGMMcYYY4wxxphxZwUKY4wxxhhjjDHGjDsrUPS4ZrwDCFVCHJUQA1RGHJUQA1RGHJUQA1gck1mlvKcWR49KiAEqI45KiAEqI45KiAEqJ47JplLe10qIoxJigMqIoxJigMqIoxJigEkah41BYYwxxhhjjDHGmHFnLSiMMcYYY4wxxhgz7qxAYYwxxhhjjDHGmHE3pQoUIvJGEXlCRHwRGXAqFBE5VUSeFpFnReSygvWNInK3iPwnvJ02jBj2eAwR2V9EVhcsrSLywXDbFSKysWDb6UONYSivRUTWi8hj4blWDfX5I41BRBaIyL0i8mT4s/tAwbYRvRcD/ZwLtouIfDfc/qiIHF7qc0cxhnPDcz8qIg+IyKEF24r+bMoUxwki0lLwXn+m1OeOYgwfLTj/4yLiiUhjuG1U3gsRuU5EtonI4wNsL/vvxGQnFZCHSz2OlDkXl/paBvr9HuP3oiy5uIR/92Pyb66EOMqei0uIoex5uMQ4LBdPAlIBubjE3GPXxD372DWxXRPntk/uPKyqU2YBDgT2B+4DVgywjwusBfYGYsAa4KBw21eBy8L7lwFfGUYMQzpGGM8WYK/w8RXApaPwXpQUB7AeaBrp6xhuDMAc4PDwfi3wTMHPY9jvxWA/54J9TgfuAgQ4BvhHqc8dxRiOBaaF90/LxTDYz6ZMcZwA3DGc545WDH32fw1wTxnei+OAw4HHB9he1t+JqbBQAXl4OMehDLm41BgG+v0ey/eCMuTiEnNP2f/NlRhHWXNxiTGcQBnz8HCOheXiCbtQAbl4qMfArontmtiuiYvtP+ny8JRqQaGqT6rq03vY7SjgWVVdp6pp4BbgrHDbWcAN4f0bgNcOI4yhHuNkYK2qPj+Mc41mHKP9/JKOoaqbVfWR8H4b8CQwbxjn6muwn3NhfD/WwINAg4jMKfG5oxKDqj6gqrvChw8C84dxnhHHUabnjuQ45wA3D+M8g1LV+4Gdg+xS7t+JSa9C8vBwjlOOXFwJebik45QpF1dCHi4pjjHIxZWQh4dzLMvFE1SF5OJKyMPDiWO0n1/SMeya2K6JBzDp8vCUKlCUaB7wYsHjDfT845+lqpshSBLAzGEcf6jHeAv9f+kuDpvSXDecZmRDjEOB/xORh0XkwmE8fzRiAEBEFgGHAf8oWD3c92Kwn/Oe9inluaMVQ6F3EVQqcwb62ZQrjpeIyBoRuUtEDh7ic0crBkSkCjgV+FXB6tF6L/ak3L8TJlDuPDyc45QjF1dCHh7ycUYxF1dCHi41jkLlyMWVkIeHdCzLxVOCXRP3ZtfE/fexa2K7Js4Ztd+JyIhDqzAi8kdgdpFNl6vqb0s5RJF1OloxDPE4MeBM4BMFq78PfCGM6QvAN4DzyxjHS1V1k4jMBO4WkafCilpJRvG9qCH4x/dBVW0NV5f8XhQ7ZJF1fX/OA+0z4t+RIcQQ7ChyIkEyflnB6hH9bIYYxyMETSrbJejX+BtgSYnPHa0Ycl4D/E1VC6u6o/Ve7Em5fycmhUrIw3uKY4jHGXYuroQ8PIpxjHYuroQ8XGocwY7ly8WVkIdLjSPHcnGFq4RcXAl5eBTjsGvi/vvYNbFdExcald+JSVegUNVXjPAQG4AFBY/nA5vC+1tFZI6qbg6bsGwbagwiUtIxQqcBj6jq1oJj5++LyLXAHQM9eTTiUNVN4e02EbmVoNnO/YzheyEiUYJEfJOq/rrg2CW/F0UM9nPe0z6xEp47WjEgIsuAHwCnqWpzbv0gP5tRj6PgDyCq+jsR+R8RaSr1NYxGDAX6fYMyiu/FcOMcrd+JSaES8vCe4hirXFwJeXi04ihDLq6EPFxqHOXOxZWQh0uKo4Dl4gpXCbm4EvLwaMVh18R2TRyew66JB49zxL8T1sWjv4eAJSKyOKzWvgW4Ldx2G/DO8P47gVKqz30N5Rj9+hSFSSvnbKDoyKqjEYeIVItIbe4+8MqC843JeyEiAvwQeFJVv9ln20jei8F+zoXxvUMCxwAtYbO7Up47KjGIyELg18DbVfWZgvWD/WzKEcfs8GeBiBxFkDuaS3nuaMUQnrseOJ6C35VRfi/2pNy/EyZQ7jw81OOUKxdXQh4uNY5y5OJKyMMlxTEGubgS8nBJcYTnt1w8Ndg1cc+57JrYrolz57Nr4kD5fid0hCN8TqSF4B/sBiAFbAX+EK6fC/yuYL/TCUbGXUvQDC63fjrwJ+A/4W3jMGIoeowiMVQR/LLX93n+jcBjwKPhD3vOMN+LPcZBMPrqmnB5YjzeC4LmWxq+3tXhcvpovBfFfs7ARcBF4X0Brgq3P0bBKNcD/Y4M4z3YUww/AHYVvPZVe/rZlCmOi8PzrCEYmOjYsX4vwscrgVv6PG/U3guCi5/NQIYgV7xrrH8nJvtCBeThwY5TJI6y5eJSYhjs93ss3wvKlIv39O9+rP7NlRBH2XNxCTGUPQ+XEkf4eCWWiyf0QgXk4oGOUSQGuyZWuybGromnTB6W8CDGGGOMMcYYY4wx48a6eBhjjDHGGGOMMWbcWYHCGGOMMcYYY4wx484KFMYYY4wxxhhjjBl3VqAwxhhjjDHGGGPMuLMChTHGGGOMMcYYY8adFShMRRKRy0XkCRF5VERWi8jRQ3z+IhHZ47y/IjJHRO4I75+Qu18pROQWEVky3nEYY6Ymy8UBy8XGmPFieThgeXjqiIx3AMb0JSIvAc4ADlfVlIg0AbEyne7DwLVlOvZo+D7wMeCC8Q7EGDO1WC7uxXKxMWbMWR7uxfLwFGEtKEwlmgPsUNUUgKruUNVNInKkiDwgImtE5J8iUhtWhf8iIo+Ey7F9DyYiroh8TUQeCqvP/69g8+uB3xd5zhUicp2I3Cci60TkkoJt7wiPs0ZEbgzX7SUifwrX/0lEFobrrxeR74vIveFxjg+P+6SIXF9wzFeKyN/D1/ALEakJN/0FeIWIWDHRGDPWLBdbLjbGjC/Lw5aHpx5VtcWWilqAGmA18AzwP8DxBNXidcCR4T51BC2AqoBEuG4JsCq8vwh4PLx/IfCp8H4cWAUsDpeHC857AnBHeP8K4IFw/yagGYgCBwNPA03hfo3h7e3AO8P75wO/Ce9fD9wCCHAW0AosJSgOPgwsD49/P1AdPufjwGcK4robOGK8fy622GLL1FosF1sutsUWW8Z3sTxseXgqLlaBMhVHVdtF5Ajg5cCJwM+ALwGbVfWhcJ9WABGpBr4nIssBD9ivyCFfCSwTkTeEj+sJEnc7sH2QUO7UoGKdEpFtwCzgJOCXqrojjGNnuO9LgNeF928EvlpwnNtVVUXkMWCrqj4Wxv4EwR+N+cBBwN9EBII/PH8veP42YC5B8jbGmDFhudhysTFmfFketjw8FVmBwlQkVfWA+4D7wiT2PkCL7PohYCtwKEEFtrvIPgK8X1X/0GulyGFAYpAwUgX3PYJ/LzJAHP1eQpHj+H2O6YfH9IC7VfWcAY6VALpKOKcxxowqy8W9WC42xow5y8O9WB6eAmwMClNxRGR/6T1K73LgSWCuiBwZ7lMb9kGrJ6gi+8DbAbfIIf8AvEdEouFz9wurzM8QVGuH4k/Am0RkenisxnD9A8BbwvvnAn8dwjEfBF4qIvuGx6wSkcKq937AE0OM0xhjRsRyseViY8z4sjxseXgqshYUphLVAP8tIg1AFniWoM/cj8L1SYLq6SsI+uP9SkTeCNwLdBQ53g8Iku4jErQX2w68VlVbRGStiOyrqs+WEpiqPiEiXwL+LCIe8C9gJXAJcJ2IfDQ8/nmlvlhV3S4iK4GbRSQerv4U8IyIzAK6VHVzqcczxphRYrnYcrExZnxZHrY8POWIaiktc4yZnETkbILBdj413rEUIyIfAlpV9YfjHYsxxpSL5WJjjBlflodNpbAWFGZKU9Vbc03TKtRuggGGjDFm0rJcbIwx48vysKkUk7IFRVNTky5atGi8wzDGmF4efvjhHao6Y7zjGAuWh40xlWgq5WGwXGyMqUyD5eJJ2YJi0aJFrFq1arzDMMaYXkTk+fGOYaxYHjbGVKKplIfBcrExpjINlottFg9jjDHGGGOMMcaMOytQGGOMMcYYY4wxZtxZgcIYY4wxxhhjjDHjblKOQWGGJpPJsGHDBrq7u8c7FGMmhUQiwfz584lGo+Mdihljlk+NqQyWh00pLGcbU17DycVWoDBs2LCB2tpaFi1ahIiMdzjGTGiqSnNzMxs2bGDx4sXjHY4ZY5ZPjRl/lodNqSxnG1M+w83F1sXD0N3dzfTp0y0xG7MHqoqf8fA6Mvhpr+g+IsL06dPt25gpyvKpMeWlqmSzWbq7u+no6EBV++1jediUynK2McOj6uF5XaTTO/G8rqL7DDcXWwsKA2CJ2ZgBqK9o1kfTHprxQYMLZNcZuKma/Xua2uznb8zoyhUlMpkMmUwG3/fz26qqqoo+x/4dmlLZ74oxe6aqqGbx/RSe14Xvp8L1HiIRXDdZ9HnD+fdlBQpjjOlDfUUzBUUJAAEcAQHJjmt4xhgz6Xmeh+d5pNNpstksqoqIICK4rpvfxxhjTHmoKr6fxve78bxOVIOcK+IgEkUECurFo8a6eJiKsGHDBs466yyWLFnCPvvswwc+8AHS6TQA9913H2eccUa/59x3333U19ezfPny/PLHP/4RgJqammHF8e1vf5vOzs7849NPP53du3cPuP/VV1/Nj3/8YwCuv/56Nm3aVPK5zj77bJYvX86+++7b63U88MADw4odYNOmTbzhDW8Y9vOHa/fu3fzP//zPkJ/nui7Lly/n4IMP5tBDD+Wb3/xmr2/GRuIzn/lM/vdhILfddhtXXnllUBX2fPzuLF5rCm93Cr8jw3PrnmP5y46AiIAbFCeMqXRTMZ8CnHDCCSxcuLBXk//Xvva1+fhHmh9/85vf8O9//3vYzx+Owvek0Pr16znkkEOKrv/pT386pHOsX7+eZDLJYYcdxoEHHshRRx3FDTfcMOyY+3r3u9+9x/ft6quv5oYbbsh33WhtbaW1tZWOjg6y2Sx/+9vfOOecc3Acx77tNpPOVM7Z+++/P8uWLeOAAw7g4osvHvR8Q5G7vhtMKX8TFi1axI4dO4Z8/uG8HytXrmTx4sUceuih7LfffrzjHe9g48aNQz53MatWreKSSy7Z437HHnts2HWjk3S6me7uTaTT28lm2wB41avexL/+9QQiLmVNxUFzjcm1HHHEEWpK9+9//3tcz+/7vh555JF63XXXqapqNpvV888/Xy+99FJVVb333nv11a9+db/nDbReVbW6unpYsey11166ffv2YT33+OOP14ceemjIzxvodWQymWHFMR6ee+45Pfjgg4f8vMKf09atW/Xkk0/Wz3zmM6MZ2oB831c/46nXmdHM7m7NNHcFy66u4HFLt/7n0af04AMP0kxLd++luUu9zsF/PsX+XQGrtAJy5FgsUzUPWz7tMdb59Pjjj9elS5fqX/7yF1VV3bVrlx511FHDjr+vd77znfqLX/xiVI41UgPl3MF+jqUea+3atXrooYfmf4fKyfM8TaVS2t7errt27dKdO3fqzp07dffu3drS0pJf7rjjDn3Vq17Va11LS4vu3LlTfd8f8PhTPQ/rFM7FpbKc3WM8cnbuOalUSj/84Q/rcccdN6zzl8tw35PhvB+Ff2N839dvfvObumTJEk2lUkM+/1D4vq+el9J0plW7u7doZ+eL4bJBu7o2a3f3lvzy8pe/RP/2t9/3WtfZ+aKm0y2DnmOoudi6eJhedt++lvSmjlE9ZmxuNQ2v2WfA7ffccw+JRILzzjsPCL5V/9a3vsXixYv53Oc+N6Jzt7e3c9ZZZ7Fr1y4ymQxf/OIXOeuss+jo6OBNb3oTGzZswPM8Pv3pT7N161Y2bdrEiSeeSFNTE/feey+LFi1i1apVNDU18eMf/5ivf/3riAjLli3jxhtv5IorrqCmpia/37nnnksymeRLX/oSP/jBD7j11lsBuPvuu/n+97/Pr3/960Hjvf7667nzzjvzg3/98pe/5Pzzz2fdunVUVVVxzTXXsGzZMq644grWrl3Lxo0befHFF/nYxz7GBRdcwPr16znjjDN4/PHH8TyPj3/84/zhD39ARLjgggt4//vfz+c//3luv/12urq6OPbYY/nf//1fRIQTTjiBo48+mnvvvZfdu3fzwx/+kJe//OWsX7+et7/97XR0BL8X3/ve9zj22GN7xX3ZZZexdu1ali9fzimnnMJXv/pVPvaxj3HXXXchInzqU5/izW9+86CvfebMmVxzzTUceeSRXHHFFaRSKd7znvewatUqIpEI3/zmNznxxBO5/vrr+c1vfoPneTz++ON85CMfIZ1Oc+ONNxKPx/nd735HY2MjK1eu5IwzzuANb3gDixYt4p3vfCe33347mXSGW278KfsvXsINP7mRh1c/zHe//h22Nm/jfR9+P+vWPxe8zm9+l7mz5+TjW/fcOt709nP4/neuoiqe5IJL/h/pbNAX+le/+hVLliwZ8u9nJRCR64AzgG2q2v8r2Z79jgQeBN6sqr8cq/gmsrvuuostW7aM6jFnz57NaaedNuD2qZ5P3/KWt3DLLbfwspe9jF//+te87nWv44knngDolR+vv/56brvtNjo7O1m7di1nn302X/3qV4Hg28f29nYAfvnLX3LHHXdw4YUXctttt/HnP/+ZL37xi/zqV78C4H3vex/bt2+nqqqKa6+9lgMOOKBXPKXmq2uvvZZrrrmGdDrNvvvuy4033khVVVX+Pbn00kt5+OGHOf/886mqquJlL3tZ0Z/RZZddxpNPPsny5ct55zvfyXve856ieXQwe++9N9/85jf5yEc+wnnnncfOnTsH/Dv03HPPsXnzZp555hm++c1v8uCDD3LXXXcxb948br/9dqLRKCeccAJf//rXWbFiBTU1Nbz//e/nzjvvJB6P85Of/ISZM2fyla98hZqaGi655BLWrl3Lhz70IZqbm3Ecp19rjocffpgPfOAD3Hjjjbz44ot86lOfAoJ+zvfffz+1tbWDvj5jBvLMM1+grf3JUT1mbc2B7LffpwfcPtVzdk4sFuOrX/0q++67L2vWrMm3qr3uuuuAoCXWBz/4QdavX8+pp57Ky172Mh588EEOPfRQzjvvPD772c+ybds2brrpJo466iiuv/56Vq1axfe+9z1WrlxJXV0dq1atYsuWLXz1q1/lDW94Q0nXzDldXV2cffbZvP71r+etb31rv/ev8Br3l7/8Za/34+9//zsPPPAAl156KdlsliOPPJLvf//7xOPxAd8PEeFDH/oQt956K3fddRdnnXUWN998M1/+8pdRVV796lfzla98BQj+Zr3vfe/jj3/8I9OmTePLX/4yH/vYx3jhhRf49re/zZlnnsl9993H17/+de644w6uuOIKnn/+edatW8sLL7zAxRefz3vfez4AM2bsz44d6xCBb3zje/z0p7/EcRxe9aqT+OIXP5WPz/d9LrjgA8yfP49PfeqD/L//9x4eeWQNIsL555/Phz70oWH8xvawLh5m3D3xxBMcccQRvdbV1dWxcOFCnn322UGf+5e//KVX87a1a9f22p5IJLj11lt55JFHuPfee/nIRz6CqvL73/+euXPnsmbNGh5//HFOPfVULrnkEubOncu9997Lvffe2y/GL33pS9xzzz2sWbOG73znO722v+ENb2DFihXcdNNNrF69mtNPP50nn3yS7du3A/CjH/0o/8dnT/7+979zww03cM899/DZz36Www47jEcffZQvf/nLvOMd78jv9+ijj3LnnXfy97//nc9//vP9mpJdc801PPfcc/zrX//i0Ucf5dxzzwXg4osv5qGHHuLxxx+nq6uLO+64I/+cbDbLP//5T7797W/n/zDOnDmTu+++m0ceeYSf/exnRZuIXXnlleyzzz6sXr2ar33ta/z6179m9erVrFmzhj/+8Y989KMfZfPmzXt87XvvvTe+77Nt2zauuuoqAB577DFuvvlm3vnOd+ZHAX788cf56U9/yj//+U8uv/xyqqqq+Ne//sVLXvKSfs2h1Q+aezfWNvDPPz3Ahe98N9/45jdBBFyCcSUc+NDHP8LLX/pyHvnbQzx0/4McfMBB+WM8/Z9neNPbz+EH/3MNRx6xgmt+dC3vf9/7Wb16NatWrWL+/Pl7fG0V7Hrg1MF2EBEX+Arwh7EIyAzfVM+nJ598Mvfffz+e53HLLbcMWhhdvXo1P/vZz3jsscf42c9+xosvvjjgvsceeyxnnnkmX/va11i9ejX77LMPF154If/93//Nww8/zNe//nXe+973Fn1uKfnqda97HQ899BBr1qzhwAMP5Ic//GG/45x33nl897vf5e9///uAcV555ZW8/OUvZ/Xq1XzoQx8aNI8O5vDDD+epp54CGPTv0Nq1a7nzzjv57W9/y9ve9jZOPPFEHnvsMZLJJHfeeWd+v0wmQ0dHBx0dHSxbtow///nPHHvssfzkJz/Bdd1e3TYuuOACLrjgAv72t79x9913M3v27Py2f/zjH3zoQx/i5ptvZvHixXzve9/je9/7HqtXr+Yvf/kLyWTxQdqMqVRTPWcXcl2XQw89lKeeeoqHH36YH/3oR/zjH//gwQcf5Nprr+Vf//oXAM8++ywf+MAHePTRR3nqqaf46U9/yl//+le+/vWv8+Uvf7nosTdv3sxf//pX7rjjDi677LJ+2we6Zoag0POa17yGt771rVxwwQVF37/B3g8RYeXKlfm/N9lslu9///t7fD+gJxdv2rSJj3/849xzzz2sXr2ahx56iN/85jcAdHR0cMIJJ/Dwww9TW1vLpz71Ke6++25uvfVWPvOZzwC5HhM+2Wwb2WwHTz75GL/97Y+4//7b+fKXv43ngRMO/i4Cf/jDn7j99t/zl7/8joceuocPf/h9+ZiyWY93vvO9LFmyD5/73GWsWfMEmzZt4vHHH+exxx4r+fPOYKwFhellsJYO5aKqRfuUDrS+0Mtf/vJeH7CLHeOTn/wk999/P47jsHHjRrZu3crSpUu59NJL+fjHP84ZZ5zBy1/+8kHPc8899/CGN7yBpqYmABobGwfdX0R4+9vfzk9+8hPOO+88/v73vxftR1zMKaeckj/+X//61/w3dSeddBLNzc20tLQAcNZZZ5FMJkkmk5x44on885//ZPny5fnj/PGPf+Siiy4iEon0ivnee+/lq1/9Kp2dnezcuZODDz6Y17zmNUBwkQxwxBFHsH79eiC4sLz44otZvXo1ruvyzDPP7PE1/PWvf+Wcc87BdV1mzZrF8ccfz0MPPcSZZ565x+cGrb6CY+Sq1wcccAB77bVX/twnnngitbW11NbWUl9fn49/6dKlrFmzBvX8YEyJzgze7hT4ymtPOxMc4fAjDuc3d/6233gS995/Hz/63+BDgeu61NfXs2v3Lrbv2MHrz3kDP7vxFg4+MChaHHPk0Vz5ta+wadtmXve6103Y1hMAqnq/iCzaw27vB34FHFn+iCaPwVo6lMtUz6eu6/Kyl72Mn/3sZ3R1dbFo0aIBj3vyySdTX18PwEEHHcTzzz/PggULBo0lp729nQceeIA3vvGN+XWpVKrovoPlq0cffRQIihif+tSn2L17N+3t7bzqVa/qdYyWlhZ2797N8ccfD8Db3/527rrrrj3GOVAeXbZs2aDPy+Xh3DEG+jt02mmnEY1GWbp0KZ7nceqpp6KqHHzwwfznP/+htbWVbDZLV1cXmUyGWCzG6aefjoiwfPnyfh+E2tra2Lx5c/49SiQS+W1PP/00H/jAB7j11luZMydo3Xb00UfzkY98hHPPPZfXve51E71YbMbZYC0dymWq5+xiMUOQd84++2yqq6uB4Pr0L3/5C2eeeSaLFy9m6dKlABx88MGcfPLJiAhLly7NX7v29drXvhbHcTjooIPYunVrv+0DXTNDcL39sY99LF+0GOr79/TTT7N48WL2228/AN75zndy1VVX8cEPfrDk9+Ohhx7ihBNOYMaMGQCce+653H///bz2ta8lFovliyRLly4lHo8TjUY55JCDWL9+Pen0TtKZHfh+mkymBfA57bRTSCZrSCZrmDGjia1btzN//tz8ee+5537e/va35GdJamyclt928cUf5fWvP5PLLgviX7x4Ic89t573v//9vPrVr+aVr3zlHl/XnoxrCwoROVVEnhaRZ0WkXzlLRM4VkUfD5QEROXQ84jTldfDBB7Nq1ape61pbW3nxxRfZZ5+RFUxuuukmtm/fzsMPP8zq1auZNWsW3d3d7Lfffjz88MMsXbqUT3ziE3z+858f9Dil/KHo67zzzuMnP/kJN998M2984xvzSW9Pcsk4d96+cnH0jafv42Ixd3d38973vpdf/vKXPPbYY1xwwQW9vk3LNTdzXZdsNpiq4lvf+hazZs1izZo1rFq1Kj9w02CKxV2KdevW4bouM2fOHPQYhc3iHMchFovhZ3zIKpnONF5LGs0G/dhyg1vGqxIg4DouWa/0aTjq6+qYP28+DzzYM3jpOW94C7/5xa9JJpO86lWv4p577hnW650IRGQecDZwdQn7Xigiq0RkVe6bEzO2LJ8G3Tze//7386Y3vWnQYxbmkcKcVxjbQK0NfN+noaGB1atX55cnnyzeNLxvvso9dhwnf86VK1fyve99j8cee4zPfvaz/c47nPcs97zh+Ne//sWBBx444DFyscTj8Xyf4Wg0SkdHBy0tLWQyGbq6uvJxO46D4zhEo9H8c13X7TcLx2Dxzp49m3g8ni/qAHzwgx/k2muvpauri2OOOSbf6sOYicJydg/P83jsscc48MADh3QNWCynDvacYsce7DW+9KUv5a677so/bzjv33DlcvFgx8jl1eA1+EQiSndqK+n0VrLZDJ7XheCGuTgKCPF4LP981+3/vqkOPD3oMces4M9//lv+79S0aQ2sWvU3TjjhBK666ire/e53D/v15oxbgSJsMnwVcBpwEHCOiBzUZ7fngONVdRnwBeCasY3SjIWTTz6Zzs7OfHXV8zw+8pGPsHLlygHnNy9VS0sLM2fOJBqNcu+99/L8888Dwci9VVVVvO1tb+PSSy/lkUceAaC2tpa2traiMf785z+nubkZgJ07d/bbp+9z586dy9y5c/niF7/IypUrhxX/cccdx0033QQEIzY3NTVRV1cHwG9/+1u6u7tpbm7mvvvu48gje3+5/cpXvpKrr746n3R27tyZTyZNTU20t7fzy1/ueSiBlpYW5syZg+M43HjjjUWndev72o877jh+9rOf4Xke27dv5/777+eoo44a9Dzbt2/noosu4uKLL0ZEer32Z555hhdeeIH9998/v7/6ip/2QMFrSeG3pdF0OAOIG3TbQEqfeeOk40/k6h8GKcbzPFpbW4GgX+SvfvoLfnLLTdz8i1sAWLf+OfZevDeXXHIJZ555Zq8L5kno28DHNTe31CBU9RpVXaGqK3JVfjO2LJ8G3yp+4hOf4JxzzhnW65w1axZPPvkkvu/n+1D3jamuro7Fixfzi1/8AgguQNesWTOs80HQcmDOnDlkMpl83ivU0NBAfX09f/3rXwGK7tM3RmCPebSY9evXc+mll+ZbXhT7O1RTU5OfAjQ34wYE3QRzxYjhzLhRV1fH3Llz898Kp1Kp/KwC9fX1/OIXv+Dzn/88f/nLXwB47rnnWLp0KR//+MdZsWKFFSjMhGM5O5DJZPjEJz7BggULWLZsGccddxy/+c1v6OzspKOjg1tvvXWPLRVGotg1c87nP/95pk+fnu/GN9D7V6jw/TjggANYv359vsvOjTfemG8NNxBV5bvf/S6bN2/m1FNP5eijj+bPf/4zO3bswPM8br75Zo477jh8PwNAOr2D7u5NZLOd+H43qIdI0GXDcYbeYeIVrzieH//45nz+3blzV37bypVv5dRTT+atb72AbDbLjh078X2f17/+9XzhC18o+n4M1Xi2oDgKeFZV16lqGrgFOKtwB1V9QFVz78iDgLXdm4REhFtvvZVf/OIXLFmyhP32249EIpHvR5bNZgccSKZv/7u+H7jPPfdcVq1ale8LlhvA7LHHHuOoo45i+fLlfOlLX8oPsnXhhRdy2mmn9RtE7OCDD+byyy/n+OOP59BDD+XDH/5wv1hWrlzJRRddxPLly+nq6sqff8GCBRx0UN/aW2muuOIKVq1axbJly7jssst6DRZ21FFH8epXv5pjjjmGT3/608ydO7fXc9/97nezcOFCli1bxqGHHspPf/pTGhoauOCCC1i6dCmvfe1r+xU1innve9/LDTfcwDHHHMMzzzzTq4VHzvTp03npS1/KIYccwkc/+lHOPvvs/HlPOukkvvrVr/bqR5zT1dWVn2b0Fa94Ba985Sv57Gc/mz+v53ksXbqUN7/5zfzoRz8i5kbxMx5+2gumAm3PBGVeR8KpQAkKEsOY+uibX/k6f/7Ln1n+kiM46viX8MRTPdPiVVdX89uf3cp3rvpvbrvzdn5x6y9ZtiL4nXvqqad69cmehFYAt4jIeuANwP+IyGvHNSIzIMunwXtw6aWX5psjD9WVV17JGWecwUknnZTvSgBBy4yvfe1rHHbYYaxdu5abbrqJH/7whxx66KEcfPDB/Pa3vwWC6e1y/X5L9YUvfIGjjz6aU045pd9Amzk/+tGPeN/73sdLXvKSAcdaWLZsGZFIhEMPPZRvfetb/fLo9ddfX/Tnv3bt2vw0o29605t4//vfn+9HnPs7tHTpUj72sY9x1VVX0dLSQjqdzhesXdcFgm8wR+qaa67h6quv5thjj+WUU07p1Rx75syZ3HLLLVx66aWsWrWKq6++mqVLl3LooYeSTCbHpVuVMSMx1XP2ueeey7JlyzjkkEPo6OjI59HDDz+clStXctRRR3H00Ufz7ne/m8MOO6yUt3RYil0zF/r2t79Nd3c3H/vYxwZ8/woVvh+qyo9+9CPe+MY3snTpUhzH4aKLLioax0c/+tH8NKMPPfQQ9957L7FYjDlz5vBf//VfnHjiiRx66DIOPfQgTj31aFKprYDiealw6k8nvB3ZNKCvfOVJvPrVr+LYY1/FUUedzLe+1XvMjA984CKWL1/KeeddzKZNW3jFK17N8uXLWblyJf/1X/81/BOHZCTNTkZ0YpE3AKeq6rvDx28HjlbViwfY/1LggNz+RbZfCFwIsHDhwiNyVUKzZ08++WS+KWcl+s53vsPGjRvzI6xPJBdffDGHHXYY73rXu0b1uIUju09m6iua9dGMj2Y8CBtIDLV1xKjLKk4ygpMcuCpd7N+ViDysqivKHd5QhWNQ3DHYLB7hfteH++2x6c2KFSu0b7PVqcDyafmUK5+a/lQVz/PwPI9MJkM2m803MR5Oy4hy8TyPhoaGAeOZCHlYRL5A8AWdD2wDVqrqpgH2dYFVwEZVPaOU40/VXFwqy9nlYzl75FR9fD+D76fw/C40bDEB5AsS4833M0QidUSjdQPuM9RcPJ6DZBb7a1K0WiIiJwLvAorPq0XQtJiwC8iKFSvGp+piRt273vUuHn/8cX7+85+PdyhDdsQRR1BdXc03vvGN8Q5lwlBV8MKiRNpDs+E/ZSFsJTGu4U1KInIzcALQJCIbgM8CUQBV3eO4E2bisHxqBqKq+L5PNpslm82SyWR6FSRGo2WEGdDXVPXTACJyCfAZoPjXq/AB4Elg4E8CZtKwnD31BGP6hAUJrxvf7xl8OShIREfUMmKiGM/L/Q1A4XDZ84F+FWMRWQb8ADhNVZvHKDZTIYpNtTZRPPzww2U79hVXXFG2Y4+1Xq0kwjElgKCVhDuOrSSmCFUtuaO+qq4sYyimzCyfmpxcQSLXQqKwIJEb1NKMDVVtLXhYzcBf1s0HXg18Cejfxt5MOpazJ7+egkQ6LEj0DJAcdNeIVEyLtbE0ngWKh4AlIrIY2Ai8BXhr4Q4ishD4NfB2Vd3z3IbGmIqnGhQkyIaDXHp9WklMvTxsjDFl1bcgkc1m8f2gz5wVJMafiHwJeAfQApw4wG7fBj4G1JZwvMJuz6MTpDFmxHoKEhl8vwvPS5GrSU7lgkRf41agUNWsiFwM/IFgaLvrVPUJEbko3H41QTO36QSDsgFkK6nfoDFmz3p128gES54j1krCGGNGWeEYEn27bOQKErmBLU35icgfgf4jRcPlqvpbVb0cuFxEPgFcTNDVrvD5ZwDbVPVhETlhT+ezbs/GVIZeLST8bvyCggRI2G3DLoL7Gtce3ar6O+B3fdZdXXD/3cDIJ1M1xowZVQVfUU+DcSQyvnXbMMaYMhpsUEtrITH+VPUVJe76U+BO+hQogJcCZ4rI6UACqBORn6jq20YxTGPMCOUGtVRN9xtDwgoSpbMh54wxI6aeHxQk+s62Yd02jDFm1OW6a+QGtcxms/ltVpCYWERkiar+J3x4JvBU331U9RPAJ8L9TwAuteKEMeNP1Qu7a6Tx/S78XrNsWJeN4bIChemn5f/Wk92d2vOOJYo0xKl/5aJB9zn//PO54447mDlzJo8//viA+33nO9/h2muvRVW54IIL+OAHPwjAmjVruOiii2hvb2fRokXcdNNN1NXV8be//Y33vOc9xONxbr75Zvbdd192797Nm9/8Zn7/+98XTRqZTIZPf/rT/OpXvyIej1NVVcXnPvc5TjvtNBYtWsSqVatoamoayVsy4eUHtgy7bfQbR8LFihLGAPfccw8tLS2jdrz6+npOOumkQfexfDq55FpH+L5fdPwIEbHuGhPblSKyP0Fp/3nCGTxEZC7wA1U9fTyDm2rWrvsW3d1FZ3kdlkRiLvvs/aFB97GcPTEE3TWy+JrB91L4fjeqXn57UJCYGrNslNseS+wiskpE3ici08YiIDP+srtTRKYlRm0ppdixcuVKfv/73w+6z+OPP861117LP//5T9asWcMdd9zBf/4TfOnw7ne/myuvvJLHHnuMs88+m6997WsAfOMb3+BXv/oVX/7yl/n+978PwBe+8AU++clPDljR/PSnP83mzZt5/PHHefzxx7n99ttpa2sbyls4qagq6vn4aQ+vM0O2JYW3O4XfnkFT4awbrkCkYDwJS85jznJ1ZWppaaGhoWHUllKKHZZPJ65cMSKTydDV1UVbWxstLS20tbXR0dFBJhN8O+e6Lq7r4jiOfTs3hsqRZ1X19ap6iKouU9XXqOrGcP2mYsUJVb1PVc8YrfOb3rq7N5FMzB+1pZRih+XsyqTq4XndZDJtpFLb6e7eRCq1lUx6J57XCYBIFMcJlqD7xjgHPUmU0gbwLcBc4CERuUVEXiX219CMsuOOO47GxsZB93nyySc55phjqKqqIhKJcPzxx3PrrbcC8PTTT3PccccBcMopp/CrX/0KgGg0SldXF52dnUSjUdauXcvGjRs5/vjji56js7OTa6+9lv/+7/8mHo8DMGvWLN70pjeN1kuteL0KEh0ZvJY0Xkt64IKEgxUkKoPlagNYPp1Icq0iuru7aW9vp6WlhdbWVtrb20mlUvi+nx/Q0goSFcHyrBl1lrPHXzB2RJpstoN0eifd3Zvp7t5MOr2DbLYF1Qwibr4Y4TiRsMXEeEc+Oe2xQKGqz4ajC+9HMHjPdcALIvI5ERn8X5Mxo+iQQw7h/vvvp7m5mc7OTn73u9/x4osv5rfddtttAPziF7/Ir//EJz7BhRdeyLe//W0uvvhiLr/8cr7whS8MeI5nn32WhQsXUldXV/4XVCFy0376qSxeexpvd6qnIJH2guKDFSQqnuVqMxSWT8dWbprPTCZDKpWio6ODlpYWWlpaaG9vp6uri2w2a8WICmd51owXy9mjJ8jHGTyvk0xmN92prWHriG1kMrvwvC6gWOsIy8djpaRRlERkGfAN4GvAr4A3AK3APeULzZjeDjzwQD7+8Y9zyimncOqpp3LooYcSiQTDqFx33XVcddVVHHHEEbS1tRGLxQBYvnw5Dz74IPfeey/r1q1j7ty5qCpvfvObedvb3sbWrVvH8yWNOVVFfcXPePhdWbzWFN6uFF5rGr8jG4wn4YTFCOuyMeFM9Fy9a9cunnjiCV588UWam5vp6urK97U3o8vyafkUdtPItYxobW3NFyM6Ozv7ddXIFSRM5ZvoedZMTJazhyffMiIsRqRSW+nu3kgqtZV0eifZbDuo36sYYa0jxt8eB8kUkYeB3cAPgctUNTegwD9E5KVljM1McS+++CKvec1rALjooou46KKLeNe73sW73vUuAD75yU8yf/58AA444AD+7//+D4BnnnmGO++8s9exVJUvfvGL/OxnP+Piiy/mc5/7HOvXr+e73/0uX/rSl/L77bvvvrzwwgu0tbVRW1s7Fi+zrFQVvKDLhmaCQS0p/Lxn035OGpMhV+/atYtHH32UZDKJiKCqiAg1NTXU19dTX19PdXU1yWSSqqoqYrGYfaNRIsun5eH7fn7JzarheV5+ik/omVXDBrKc+CZDnjUTg+XsoQlyro/vZ4OBLP0Uvp9GNdtrP5tZY2IoZRaPN6rqusIVIrJYVZ9T1deVKS5jWLBgAatXr+61btu2bcycOZMXXniBX//61/z973/vtd73fb74xS9y0UUX9XreDTfcwKtf/WqmTZtGZ2cnjuPgOA6dnZ299quqquJd73oXl1xyCf/7v/9LLBZj8+bN/OlPf+Jtb6vsGb1UFXwNpvsMZ9gg23ORbDNsTHqTIlcnEgnq6+vzj33fJ5vNsmXLFjZs2NBrX9d1qampoa6ujrq6unzxIpFIEI/H7RvpApZPR6awEJH7nczNrFFYTLMpPie9SZFnTeWznD0wVR9VLyxEZPD9FKoZVAu/gRObVWMCK6VA8Uvg8CLrjhj9cEwliDTEye7qHtXj7ck555zDfffdx44dO5g/fz6f+9zn8lXiQq9//etpbm4mGo1y1VVXMW1aMJD2zTffzFVXXQXA6173Os4777z8czo7O7nhhhvy1eUPf/jDvP71rycWi3HzzTf3O8cXv/hFPvWpT3HQQQeRSCSorq7m85///LBee7n0LUaQ9dG+xYjc2BGWmKeKSZmrHcchFovlm6wW8n2fVCrF5s2b831uc0SE6urqfAGjpqaGRCKRX6LR6Jh8g1JfX8/u3btH9Xh7Yvl0eHLjRORuPc/LL4WFCCA/PoS1iphyJmWeNT0Sibl0dW/Y845DON6eWM4urnchIotqOmwV4fXaT8QBHJtFYxKRwmaIvTaIHAAcDHwV+GjBpjrgo6p6cPnDG54VK1boqlWrxjuMCePJJ5/kwAMPHO8wzADU79MywuvTMgLCgSutGDFmsoqTjOAkB67xFvt3JSIPq+qK0QylUnP1cPLwunXrWLNmDQ0NDSM+f+5b7tySmw0Bgg+irutSXV1NdXU1tbW11NTUEI/H860vYrHYsL4Jt3xa2foWIQoLEbn1ObkWEdYUuHJ5nkdDQ8OAP6PRysOVmmdLMdRcrKrs2v0PHIkQidTmF9etDj8ITi6Ws8eXqoZFiNySwfczYYsIr8/eUlCMGI9oTTG+nyESqSMaHXhw1aHm4sFaUOwPnAE0AK8pWN8GXFBizMaYEhW2isDPddMI7udZywjTn+XqIgZreQE90zvu2LGDLVu25L8hL2yun0gkqKqqyhcykskk8Xg8X8CIx+P2DXqFKVaAyBUhcvf7sq4ZpgRTKs9u3vxLBCf44gMBVRCIRuqJRKcRi00nFmsiFm3AdWuIRGpw3WpctxrHKaVxtplKehUh8FA/W1CIyPbbv6cIYd0zpqoBs4iq/hb4rYi8RFX/PoYxGTOpFS1EeMFglr3kx4ywYoQZmOXq4dlTASM3E0N7ezstLS1ksz0XUbkihqoSjUZJJpP5xXVdUqlU/gNv4bfw9k388OXe78ICRGFXjNzPq1ir0ML33wpKZjimXp4VEol5vdYEsyFkyKSbSXVvwvczgB8UMVRAFFRx3Woi0Qai0QZi0WlEY9OJRGqIuNW4blVYyKjCcaLj89LMqAu6Yvhhi4fgNld8CJa+LSGgZ4wIG7DS9DdggUJEPqaqXwXeKiLn9N2uqpeUNbIx1vaXDaivuDUxnJoYblUEibs4CReJR5DY5J6PPPeNoRkduSIEfq6LRtAaIleU6EUI/sBbIWLSGKjrXDlMtVw9VkSESCSSn8atmNwH5Gw2y+7du2lubmbu3Ll0dHT0ao1ReMzCwkXf28J9Cgsak6m4kXs/CgsOhev6tn4oXAcUfU9zt9YCwhQazTxseTb4Vtt140AcKD7DQ/DvOYOX7SCb2UWH/wy+ZoJLm3z3EAX1cZwEkUgdkWg90Ugd0WgDkUg9kUg1jpvEdZK4biIsZiTK2r3EroGLy82MkStA5IoPucKDH96n6L+13N8tawkx1Q0nFw/WDuvJ8HZKDOaQ3tyBk4iQbe4GzwcKPiyqgghOIoJTHcGpjuJWR3Gqo0jMxYm7SNxFYi4SnXiFjEQiQXNzM9OnT59wsY+nfEuIsBCBH7SEKNoaAnrGibBCxKSmqjQ3N5NIJMbqlFMqV1eS3Dfyhd/K+75PR0cHdXV1RfNp7gM3kG+VUewDd7H1AxUuiq3ve6xisZSS74tdWBSuKywu5G4HWwr3zZ2/7zn6xm+FBzNUZcjDlmdLEOShGI4TA6oH3K+nyX+GVPdWunVD0ORfvXwxQwBF8x9+HTdJxK3BjVQH42K4tbiRGqKRWhw3gePEcZ04Tq8lFn44HjjXTZVr4CDPBktQbMjd9hQggsKDj+KBen1mxehDHARBcEHEChCmqOHm4sG6eNwe3t4wwtgmDEm4OFL8LVE/+NDptWfI7k4FH0BVKVrISEaQZAS3KoJTFcFJRnESYSuMaFDMcGIORCqjmDF//nw2bNjA9u3bxzuUyqG5P4wEP9fwNsjpYSuI8McP+R99oAJ+pqbMVINiZLR4c/FEIpGfn7z8oUy9XF3JOjo62LJlCzt27Bj1Yw/0LcRYttgp1XALIsYMhe/7JJPJor9bo5mHLc+OrqCQEQEiuG5yj/sHBY3gW/tMeiep1LZwHIMsih9eijmEVY3w2iz4AC7ihsWLJI6bwHUTOLnWGU6SRKKK5uYGtm7dQM8Ffc9t8KtV+PtV/H7Pr+Bw85z2aYigRe7n9tEi6wsLELn7fq/1ezbQ6zdmYKpe/t9UMcPJxXscyUZE7iaY93l3+HgacIuqvmpIZ5rgxBFwBIkO/k1OrpDhd2TwWtNBa4xck/78v/Lch1vBiblIMhK0wki4+ZkBgtYYLk7UCT8IBQuuE8QyiqLRKIsXLx7VY1YiVUUzPpr20LSPZjz8lIff7eF3ZtDODF5nFr8zi6ayFCs+EXGQiCARJ/h9sMw9JXktKZIHTSd54PTxDiXPcnVlUFXa29vHOwxjpoSWlhbOOOOMQbtijSbLs+MjKGhEgaGPW9EzVaWHl+0km22DgnXBwI0+IopSeF2X+waK/teCuUqCaK8vtDT/TMm3MOh1rIIigYZdXTQsHggSnkpyL7rwHSh4LwoOJUHBQggGeUZcBDecbrPvEpmUs7CY8dfdvZmZM19FU9OJo3bMUjL6jFwiBlDVXSIyc9QimGRKLWRA+K2Xp8GH5K5sMH1kbuDE3GfjwgSVaxYbdYICRswNChxh9xIn7va00nDDD9Hhrbi5+4I44f1RLnSUW+79CrpQhGM6ZP1eC1k/X3TQ7ix+ykNT2eBxOhykp7Aanvsj4ziIGxYgog6SiFvxwUw0lquNMaa8LM9OMMFAjA7DKW4MR0+Ltr63vaLqdWvXm8b0VkqBwhORhar6AoCI7EVp7YTMHogIRIQBepUUVTj4YvDNfzYcA6FgkbCNW9+E16u1mAaFlFzBIiI9RQy38L4TtJpzJfgQH05xKY4Ex8/NNBG8ov4t23JN0XKtSHJjNmhPa5Pcbc9Akn6fIoSGxRs/98YN9ObkW6bkZr+Q3G3YUsX+CJhJzHK1McaUl+VZM6h+LTCMMUNWykfjy4G/isifw8fHAReWLyQzGMkNsuiOLPVpwbgKuVs/60Mm9zhYp4X7QE/BoeCx0mddv6CD//Xqxpd7IORnsZDcbBa91gkSE8QJXrAVGIwZkOVqY4wpL8uzxhhTZnssUKjq70XkcOAYgo+NH1LV0R/9y4wpyRUCCvu1jVs0xpiRslxtjDHlZXnWGGPKb8CBEkTkgPD2cGAhsAnYCCwM1xljjBlnlquNMaa8LM8aY8zYGawFxYcJmq19o8g2BU4qS0TGGGOGwnK1McaUl+VZY4wZIwMWKFQ116fuNFXtLtwmIsUnOjXGGDOmLFcbY0x5WZ41xpixU8qEuA+UuM4YY8z4sVxtjDHlZXnWGGPKbMAWFCIyG5gHJEXkMHrGUKwDqsYgNmOMMXtgudoYY8rL8qwxxoydwcageBWwEphP0Ocul4zbgE+WNyxjjDElGlGuFpHrgDOAbap6SJHtZwFfAHwgC3xQVf86KpEbY8zEYNfExhgzRgYbg+IG4AYReb2q/moMYzLGGFOiUcjV1wPfA348wPY/AbepqorIMuDnwAHDCtYYYyYguyY2xpixU8oYFPNFpE4CPxCRR0TklWWPzBhjzFAMK1er6v3AzkG2t6uqhg+rCUasN8aYqciuiY0xpsxKKVCcr6qtwCuBmcB5wJVljcoYY8xQlS1Xi8jZIvIUcCdw/iD7XSgiq0Rk1fbt20fj1MYYU0nsmtgYY8qslAJFrp/d6cCPVHVNwTpjjDGVoWy5WlVvVdUDgNcSjEcx0H7XqOoKVV0xY8aM0Ti1McZUErsmNsaYMiulQPGwiPwfQTL+g4jUEgyWNmIicqqIPC0iz4rIZUW2HyAifxeRlIhcOhrnNMaYSapsuTon7A6yj4g0jeZxjTFmgih7njXGmKlusFk8ct4FLAfWqWqniEwnaNI2IiLiAlcBpwAbgIdE5DZV/XfBbjuBSwi+tTPGGDOwcuXqfYG14SCZhwMxoHmkxzXGmAmoLHnWGGNMjz0WKFTVF5HngP1EJDGK5z4KeFZV1wGIyC3AWUC+QKGq24BtIvLqUTyvMcZMOsPN1SJyM3AC0CQiG4DPAtHwmFcDrwfeISIZoAt4c8GgmcYYM2WU8ZrYGGNMaI8FChF5N/ABgrmfVwPHAH8HThrhuecBLxY83gAcPdyDiciFwIUACxcuHFlkxhgzwQw3V6vqOXvY/hXgK6MTpTHGTFxlvCY2xhgTKmUMig8ARwLPq+qJwGHAaAzPXmxQoWF/K2eDsxljprhy5WpjjDEBy7PGGFNmpRQoulW1G0BE4qr6FLD/KJx7A7Cg4PF8YNMoHNcYY6aicuVqY4wxAcuzxhhTZqUMkrlBRBqA3wB3i8guRqeQ8BCwREQWAxuBtwBvHYXjGmPMVFSuXG2MMSZgedYYY8qslEEyzw7vXiEi9wL1wO9HemJVzYrIxcAfABe4TlWfEJGLwu1Xi8hsYBVQB/gi8kHgIFVtHen5jTFmMilXrjbGGBOwPGuMMeU3YIFCRBqLrH4svK0hmAJ0RFT1d8Dv+qy7uuD+FoKuH8YYY4oYi1xtjDFTWTnzrIh8gWAWOx/YBqxU1X6tMkRkPdAGeEBWVVcM95zGGFPJBmtB8TDBoJWFg1nmHiuwdxnjMsYYUxrL1cYYU17lzLNfU9VPA4jIJcBngIsG2PdEVd0xgnMZY0zFG7BAoaqLxzIQY4wxQ2e52hhjyqucebZPt+VqRjCjnTHGTAYDzuIhIm8ruP/SPtsuLmdQxhhjSmO52hhjyqvceVZEviQiLwLnErSgKEaB/xORh0Xkwj0c70IRWSUiq7Zvt1lQjTETy2DTjH644P5/99l2fhliMcaYiqGqaMbDa0+Tbe4is6md1PoWup/eSerZ3WSbu8Y7xBzL1cYYU14jyrMi8kcRebzIchaAql6uqguAm4CBCh4vVdXDgdOA94nIcQOdT1WvUdUVqrpixowZewrPGGMqymBjUMgA94s9NsaYCUFVwVP8lIeGS3A/i6Y9NOXjh/fxixwg4iBRJ9heGSxXG2NMeY0oz6rqK0o8z0+BO4HPFjnGpvB2m4jcChwF3F/icY0xZsIYrEChA9wv9tgYY8aden5BwcFD017/QkTaA69ICnMFJ+4icRe3Po7E3fxjiUeC+zEHcR28lhTROTVj/wKLs1xtjDHlVbY8KyJLVPU/4cMzgaeK7FMNOKraFt5/JfD5kZzXGGNGSlXx/RS+nxnV4w5WoDhARB4lqAzvE94nfGyjwhtjxoz62rvwELZw6Ft8KFp4cCQoNsRc3NpYn8JDsF7iLhIZrMdbRbNcbYwx5VXOPHuliOxP0GbvecIZPERkLvADVT0dmAXcKiIQXLv/VFV/P8LzGmNMP76fwfO68P1OPK8Lz+sMFr+r12Pf68LzuwClqmoxM2e+ctRiGKxAceConcUYY4pQX8NuFYO3eiBbpK+F0FNkqIniNibClg4OEgtbPSRccIXwom6yslxtjDHlVbY8q6qvH2D9JuD08P464NByxWCMmbxUfXy/u6C40IUXFh/8XsWHYJ1q8dYQIlFcN4nrVhGN1OHGZ+O4VaifIZ6YPaoxDzbN6POjeiZjzJShqmja7yk4dGf7FyJSHpoZoPAQtmpwqiK4DfGeQkSu5UPMRaLOZC88lMRytTHGlJflWWNMJQlaOQSFBd/rLFp8yLd08Lsp3hNNgoKDU4XjJonH5+Qf5woRrluF4yTD2+Jlg+7uzcRjozsY72AtKIwxppdgZgu/p3VDuqDYUDDGg6a9orlQYk6+2ODWxZGwtYOTiOSLEhKzwoMxxhhjjJka+rdy6N2qoW9LB9Vs0eMErRyCAkM0Wk8iV3RwgyKE6xQWHhIVe71tBQpjTDCzRVaD2SsGGVxSUwMUHqJOr+4WRcd4iLmIU5mJ0BhjzPjyPI9sNlvy4nkeruuOd9jGGFOU76d7Cg5hYcHv8zi33fcHmrq+TyuHaENQYCgoNOQLEE5ywFYOE01Jr0JEksBCVX26zPEYY0aZZv1+RYZexYdc4cEvUnmICE48ku9ukbsftHpwe1o9WOGhIliuNsaMF9/3h1Rg6LuoDj4ZhuM4RCKR/BKNRolExv5i3PKsMVNTTyuHzl7FhZ5iQ8/AkYO3coj1jOUQbQhbOQSFBifXtcLJtXKIV2wrh3LaY2YXkdcAXwdiwGIRWQ58XlXPLHNsxphB9JpSc7DCwwBTauZntqiL9RvfIX/fnbAzW0w5lquNMSOhqiMqMPh+kTGFCohIrwJDJBIhkUj0WzfQ4jj9/x61tLSU6+0Y6DVYnjVmklBVVDNFx27o39IhN5ZDMU7YyiFXdJhWUGjo3dJhMrVyKKdS3qErgKOA+wBUdbWILCpfSMZMbbkpNQfrZuGnspAdYErNmIMTj+DWxJDpAxQeJu6UmmZgV2C52pgpS1WH3E2ib5eJPelbNKiqqhpSgWESfBN4BZZnjalYqn44RWZX75YOAxQfBm/lUJUvOCQS88JiQ+F4DlO7lUM5lVKgyKpqi73xxoxMfkrNPoWHfoNNljKzxbR47/EdwhYQRCbFBaAZHsvVxkxgqjribhJ74rrusFswuK5rf18szxozpoJWDrmxHPqO51Ck+LCnVg5hq4ZodFpPoaFXS4egCCFirRzGUynv/uMi8lbAFZElwCXAA+UNy5iJIz+lZtFuFtmewkO6ePPX/ACSiQhufTy837v4YFNqmhJYrjZmnI31OAyxWGxIrRjs78iIWZ41ZoRyrRw8v6A1Q77Y0Pux73eiWrx1l+PEcZ0kTr9WDgUDR4bjOThOzPLfBFJKgeL9wOVACvgp8Afgi+UMyphKEMxs4ffvZtG3+8VAU2oWzGzh1sZ6WkDEXSQ/2KQVHsyosVxtzAj5vp/vJpHJZAbsCjGa4zAkk8l+LRuGMg6DGVOWZ43po3crh2JdKnqvG7yVQ1V+PIdYtLFnSsw+U2S6bsJaOUxie/zJqmonQTK+vPzhGFN+qgqeFik8ZPt1vyhWeCDi5MdycKrDKTV7FR9sSk0z9ixXGzO8gR49z8sXI/ZUYADys0fkigrV1dW9ukH03V4464QVGCY2y7NmqlD1erpV+AUzVPQpPuTWK4O1cggKDNFoI4lEVZEuFVU41srBFChlFo+7gTeq6u7w8TTgFlV9VZljM2bINOv3H1wy3b8FRNEpNV3pKTw0xAsKD5Ge4kPMRVxLnqbyWK42k8F4DPQYi8Wora0tOuZCrtgwyQZ6NMNkedZMVMEYN+kBBo8MWzYUrPP91ABHcnoVFvq1cuhTfBBxx/R1msmhlLYxTblEDKCqu0RkZvlCMqY/9bRP4SHba1aLXPGh6JSajuQLDG5drPdsFoWFB5vZwkxslqvNuLOBHs0kZ3nWVIyeVg6FA0f27WbR09oBircQc5xET7eK2HQcd0H4uKcQEbRySForBzMmSilQ+CKyUFVfABCRvSje8N2YIcvPbNFvcMk+hYdskaTqEBYbIjg1UdzpyfCx09PqIeZCRCyZmqnAcrUZFTbQozEDsjxryqanlUPvLhSFBYigpUOprRyCJRZr6lVs6GnpkBvLwVo5mMpSSoHicuCvIvLn8PFxwIXlC8lMBvmZLQpbO/QdXLKUKTWTEdyGeO/xHWxKTWOKsVxtgOGNwzDSgR6H0orBxmEwE5jlWTMkqtkBpsjsKlp82HMrhypisem47oJwhorCgSNzU2RaKwczsZUySObvReRw4BhAgA+p6o6yR2YqkqqiGX/Q8R3yM1sUERQenN5TavYdXNJmtjBmyCxXTx57Gochk8kMun044zAMpQWDjcNgpirLsyZo5ZAqKDR09is+5Mdz8LsGbOUg4uYHj3TdamKxGUWmyMzNXGFjOZipZcAChYgcoKpPhYkYYFN4uzBs3vZI+cMzY6XolJrFig+lTKlZE83f7zXGg81sYcyos1xdeQrHYSicqrLv9JWDFRn2pO9sEcVaMESj0aLTV9o4DMYMjeXZyW2gVg69WjqU3MqhKhzLYQaumwxaORSZtUIkannYmAEM1oLiwwTN1r5RZJsCJ5UlIjOqik2pOdAsF0VntogITjyCxBycaYnig0vGrfBgzDgaUa4WkeuAM4BtqnpIke3nAh8PH7YD71HVNSOKuMKN9UCPrusSj8cHna6y79SVdmFrzJiya+IJJN/KwesMxmroNXBkn1ksvE5U00WPI+LiOLnuE2Erhz7FBqdgMEkR675mzGgYsEChqheGtyeOXThmKNTz+7Vu6FV4yLV4KDazhVsws0V9n5ktCu+7lmyNqWSjkKuvB74H/HiA7c8Bx4ej1Z8GXAMcPcxzjZlKGugxV2AoLDRYgcGYicOuicef72cLpsjsyhcfcvd7t3QYrJVDMt+KIZ4rOOQHjuw9c4W1cjBmfOxxDAoReUex9ao60MWsGSH1tV+RIbif7dXqgeweptSsjfUfXDLX4sGm1DRmUhlurlbV+0Vk0SDbHyh4+CAwf1gBDpEN9GiMqTR2TTx6glYO3fnpMHu1cuhTfPC8rkFaOUTyU2C6kRpi7syCQkPfaTIT1srBmAmglFk8jiy4nwBOBh5h4G/bzAB6Tak5wPgOg85skSsyVEdxGxMFxYewC0Y8YlNqGjN1jUWufhdw10AbReRCwhHtFy5cOKQDv/jii9x22210dHTg+74N9GiMqUR2TTyI3q0cOot2q8hNkRm0cijeUs0p6EIRj88qaNWQ7DdzheNEx/ZFGmPKrpRZPN5f+FhE6oEbyxbRBNRrSs18N4tsvxYQmh5kSs2Yi5OIIPXx/uM7xN1gEEq7mDbGDKDcuVpETiQoULxskBiuIegCwooVKwbvI9FHPB6ntrYWESmp0GADPRpjxtpUuybuaeXQmW/pkJ+hos+6YCyHTNHjiETzLRkikdqwlUNu8Mie4oO1cjDGQGktKPrqBJaMdiCVqNeUmmmv31Sa+VYQg81skQgKDbnuFj3jO0SC6TZjdpFtjCmLUcvVIrIM+AFwmqo2j8Yx+5o5cyYnnngia9asoaGhoRynMMaY0Tbpromffvoz7NjxJ9TP4PkDtXKQnhkr3Cri8bpeYzc4uW4VTtJaORhjhqyUMShupyc7OcBBwM/LGdRY6/p3M5ktHWS3dfbrfjHolJqxoLtFvrVDomCMB5tS0xgzhsqVq0VkIfBr4O2q+sxIj2eMMRPVVLgmdpw4rltFLNHYU2joO3OFtXIwxpRRKS0ovl5wPws8r6obRuPkInIq8B3ABX6gqlf22S7h9tMJqtQryzHXdPvfNpLd1BFMqRkWGJyGeMH4Dm7vwSat8GCMqTzDytUicjNwAtAkIhuAzwJRAFW9GvgMMB34n7C1V1ZVV4xu6MYYMyGU7Zq4UixZcjmZbAvJxJiMh2yMMf2UMgbFn8txYhFxgauAU4ANwEMicpuq/rtgt9MIms4tIZjW7vuUYXq7xnMOYNft64hMT1h3C2PMhDTcXK2q5+xh+7uBdw8rKGOMmUTKdU1sjDGmx4Dts0SkTURaB1i2i8iDInLyCM59FPCsqq7TYO6gW4Cz+uxzFvBjDTwINIjInBGcsyi3Joa4Vpgwxkw8Y5CrjTFmSrM8a4wxY2fAFhSqWjvQtrD1wyHATeHtcMwDXix4vIH+rSOK7TMP2FwkpmFPb2eMMRPVGORqY4yZ0izPGmPM2BnWCDeq6qnqGuC/R3DuYk0W+g5JWco+uZiuUdUVqrpixowZIwjLGGMmh1HK1cYYYwZgedYYY0bXiIbgVdX/HcHTNwALCh7PBzYNYx9jjDGDGGGuNsYYsweWZ40xZnSM5xxBDwFLRGSxiMSAtwC39dnnNuAdEjgGaFHVft07jDHGGGOMMcYYM7GVVKAQkb1E5BXh/aSIDNgXr1SqmgUuBv4APAn8XFWfEJGLROSicLffAeuAZ4FrgfeO9LzGGDNZlSNXG2OM6WF51hhjymuP04yKyAUEg082AvsQdLO4GhjxaMWq+juCIkThuqsL7ivwvpGexxhjJrty5mpjjDGWZ40xZiyU0oLifcBLgVYAVf0PMLOcQRljjBkyy9XGGFNelmeNMabMSilQpFQ1nXsgIhEGmEnDGGPMuLFcbYwx5WV51hhjyqyUAsWfReSTQFJETgF+Adxe3rCMMcYMkeVqY4wpr1HPsyLyBRF5VERWi8j/icjcAfZrEJFfishTIvKkiLxkJOc1xphKVUqB4jJgO/AY8P8Ixoz4VDmDMsYYM2SWq40xprzKkWe/pqrLVHU5cAfwmQH2+w7we1U9ADiUYIB5Y4yZdPY4SKaq+gQzaFxb/nCMMcYMh+VqY4wpr3LkWVVtLXhYTZEuIyJSBxwHrAyfkwbSffczxpjJoJRZPB6jf7JsAVYBX1TV5nIEZowxpnSWq40xprzKlWdF5EvAO8JjnVhkl70JWm78SEQOBR4GPqCqHQMc70KC2UZYuHDhcEIyxphxU0oXj7uAO4Fzw+V24H5gC3B92SIzxhgzFJarjTGmvIaVZ0XkjyLyeJHlLABVvVxVFwA3ARcXOUQEOBz4vqoeBnQQdDcpSlWvUdUVqrpixowZw3qhxhgzXvbYggJ4qaq+tODxYyLyN1V9qYi8rVyBGWOMGRLL1cYYU17DyrOq+ooSj/9TggLIZ/us3wBsUNV/hI9/ySAFCmOMmchKaUFRIyJH5x6IyFFATfgwW5aojDHGDJXlamOMKa9Rz7MisqTg4ZnAU333UdUtwIsisn+46mTg38M5nzHGVLpSWlC8G7hORGoAAVqBd4tINfBf5QzOGGNMySxXG2NMeZUjz14ZFh584HngIoBwutEfqOrp4X7vB24SkRiwDjhv+C/DGGMqVymzeDwELBWRekBUdXfB5p+XKzBjjDGls1xtjDHlVY48q6qvH2D9JuD0gsergRXDOYcxxkwkpcziEQdeDywCIiICgKp+vqyRGWOMKZnlamOMKS/Ls8YYU36ldPH4LcG0Rw8DqfKGY4wxZpgsVxtjTHlZnjXGmDIrpUAxX1VPLXskxhhjRsJytTHGlJflWWOMKbNSZvF4QESWlj0SY4wxI2G52hhjysvyrDHGlFkpLSheBqwUkecImrMJoKq6rKyRGWOMGQrL1cYYU16WZ40xpsxKKVCcVvYojDHGjJTlamOMKS/Ls8YYU2alTDP6PICIzAQSZY/ImClKVUGB/G3BOnK3FNxqscP0F44yHtwPFyT4r+B+sOTWS5EDmUpmudoYY8rL8qwxxpRfKdOMngl8A5gLbAP2Ap4EDi5vaMZUPlUFH/B91FPwFfVztyCqqIAgIEpYHSg8QLBONSgOuIK4gjjBfccVcIJFnLCQ4EhQQMgVFOh/2F5FjMJCh68FMYexermYw9fgae9jDlisCI8DqAjiBPuKG1Y+cjE7Ao4VPcrNcrUxxpSX5VljjCm/Urp4fAE4Bvijqh4mIicC55Q3LGPGT/5Du5f7wO4HH9pzRYH8jsGHfok5SMzFSUZw4hEk7uAk3GBdLAIRQSJOUHiIOOCG913J389/mK8AvYouBQWMXPFCw/dDsz3vj5/20LSPn/Ig4+GnfTRcp6ksmvF7F1TyJyN4H3NFGLfw1rHCxtBYrjbGmPKyPGuMMWVWSoEio6rNIuKIiKOq94rIV8oemTFloH7wwZqsj2bD4kPhB+ewJYOTjOBWR3GqojhVkaD4kIgExYio23MbdSqmsDBaRARcwHX7NcwYrl7ve8YPihuZ3OLhd3v4XVm0O4uX8tCuLH5nJvj50L8wpIDkijv5W5nqxQzL1cYYU16WZ40xpsxKKVDsFpEa4H7gJhHZBmTLG5Yxw6OqvT4E44XfzgcbwRHcqihOYwKnJoZbE8VJRpBEBCfuIvGw6DC1P+iOOnEEibkQc4f0PPUUzYQtMdJe0FIj4+N3ZfA7s/hdWfzObFDQaA/TUm5cjcLWGa6DRARxnaBFy+T8+Q4rV4vIdcAZwDZVPaTI9gOAHwGHA5er6tdHN2xjjJkw7JrYmFGmGg68hl9wX8Oh1nKPKbgdTH5QtZ5x1vKPBbBr/ImglALFWUAX8CHgXKAe+Hw5gzJmMEERIvzgmgmSmYjkB5N0qqJEGhO4dTGc2hhuVRRJRvLdLiwxTRxBV5hISUORqa/B70Paw095aMrDT2XxO8JiRu62NdPnieH/XCfsghN2yZl4LWOGm6uvB74H/HiA7TuBS4DXjjhCY4yZ2Oya2ExpqoqqFy7ZcAkfE9yChmOv5UdmLzxCwU1YiABE3HCJIOIE93EICgoO4UBnQP+uv6oFBQxVFB80i18QK+r3xIofHpeC+Ip0QUaD8eM0iFJw8nEiLoJbEHducUb6Fhv2UKAQERf4raq+gqBX+g1jEpUxFHzgzHiQ8cOWEEGicJJRIo1J3IYYbl086IZRFQ26YbgT7oOlGQXiCBJ3Ie7i1g68X/B7FRYwuoNbryuL357Gb8/gd2bw2tPga0FrjKCSL5GgiKFeKVX8sTOSXK2q94vIokG2bwO2icirRxyoMcZMUHZNbCajoOCQRTWD76dRzeL7GRQv/LBdcE2tPoriOglcN0kkUo3rVhUsuccJxIniSLTnVqI4TiQsQEQLihG5D/Zjc+2ea6ERvGa/oMgSFC98P3gvVLP4mkX9NL5mUD+D53UFi9+F53XieV344bqs14bvdRNUNvLT5PV578CR3HsQLMF7ErXCRh+DFihU1RORThGpV9WWsQrKTC25bhl+yoeMFxQiws9/bl0Ud0ZN0CKiNpYfE0Ii9g/ZDE9QyIhAPIJbV3wf1bA41u3hd2eDIkZnBq81jd+eDo6RLKUB2tiolFwtIhcCFwIsXLhwvMIwxphRVyl51phSBR+4U/h+Olg0E37v4uR2AFFcp5pItJZ4fCaRaD2RSAPRaH2+EOE4SVw3geMkguKDDK27biURyXX3iI36sYNiTwbfT+H5KXwvhe934/vdeH43XjYoZGQzLWSz7WS9NrxsO1lvJ2g45lp+3DUN17k4Tq7AE0UkNqYFnfFSyhV2N/CYiNwNdORWquolZYvKTFrqK5ry0LTXM7WmKk51jNicKiLTk0EhoiaKk4xaawgzLkR6xsxw60b/j1iZjHuuVtVrgGsAVqxYUVnNTMyEElzo9Sy+7/dbl2vWO9Bt3/s5hRd2ufuFt33v5xbHcfqtM1POuOdZY3JUs3heKixCpFD8oGtFsBURl2i0kURiBrHYdGKxJiLROiJuDa5bRSQS3E7kgkMlCf4uxHCcGBEGacrbR/A3Lo1f0DIjd5vJtJDJ7iKbaSGTbSGbbcH3unp1eelpnRHFcWJhMSMWttKYmH+nSilQ3BkuxgyJen5QjMh45ActdCAyLUGkqY7ItEQwRkRN1FpEGDNylqtNxSgsLHie1+vW9/1eH/Bz4wj1LSy4rovrukQiESKRSP6x67o4jpO/LVwKCwlAv0JC3+JGLp6+sfaN2/M8stls/r7neT3jH4Xn6fs6creF8fWNeaJePE5hlmfNmMl1v/D9bjyvG9U0+e4D6iNOlFisiarYXsRiM4nHpxOJ1BGJ1BKJ1OI4ScsxE4CI4LpxXDdONNqwx/19PxsWMDrCpZNMtpVMeifpzE4ymV1kMrvxvc6eQkY4Nofg4jixcImH3Usq73eklALFz4B9CT5erlXV7vKGZCYi9RRNe2g6CxpesEUEd3qS6IwkbkMCtzaYtnMCDj5ozERgudqUnar2+7BerOCQKzDE43ESiUSv23g8TjQaJRqNFi1ARCKR/Af4SlVY0Oj7fmSz2fzjdDpNOp0mlUrll9zjTKb3gL254oaq9ipoFBZlKvFCcoqxPGtGnaofFiG68PxUfvBGVR/XTRKPzaS2dg7x+Gyi0WlEo/VEIvVh6wfLCVON40RwnDqi0QH6KYd8P4PndZDNduB57WSzbaTTu0hndpBJ7yCd2Y2X3d57vAz1wxYY8YIixti3sBmwQCEiEeDLwPnA84ADzBeRHxFMNZcZ6LlmclMNixHdXs+MP64QaUwQndWQbxnhVE3cpkXGTBQjzdUicjNwAtAkIhuAzwJRAFW9WkRmA6uAOsAXkQ8CB6lqa3lekRkvuQ/duQ/Y2Ww2/2E5t11ESCQSVFdXU1VVRVVVFclkkng8TiwWIxqN5m8jkcoZp2W0Bd94BYWD4cq915lMJr+k02kymQxdXV35pbu7m+7ubjKZTNGWGo7j5As8kUjEup+UgV0Tm9Gg6gcDK/pd+H4KcIJZIoBYbAY1VXuRiM8nHp8eFiKm4TgJ+/dshsVxojhOw6CtMoLWGO3BmBjZNjKZFtKZHaRT20lnmkmnd6CaLRi3xAdxcZw4bljEKIfBrh6+BtQCi1W1DUBE6oCvh8sHyhKRqTia9fG7PciGXTVQ3Po40QV1RKYncOvjONXWMsKYcTKiXK2q5+xh+xZg/uiEasabqpLJZPIFCN/3cRwn/819PB6nurqa6upqamtrqaqq6tX6IRqNVnTLhonEcRxisRixWGkXeIUtMnKtMLq7u+no6KCzs5POzk66urrIZrNFW7PkWqrkihhmSOya2JQsKPamgmb4fnfBF9RCPD6L6sS+JBMLiMWbiEUbiUYbbBwIMy6C1hgDFzGC3+UuMtlWstlggM90uplUaiup9HbSmZ1B1yMZ3S8kBjvaGcB+WtApVFVbReQ9wFNYMp6UgtYRPprKBtMsIkjMITa7isjMKiINcdz6uI0ZYUzlsFxtelHVfAEik8n0+sZdRKiurqahoYG6ujpqampIJpMkEgkSicSkbvUw0bmuSzKZJJlMDrpfNpulu7s736Wkq6uLjo4O2tvbaW9vp62trdfvhBUwSmJ51hQVtIroxPM6Uc3kB3+PRhuortmXZGIvEolZxGJBqwgrRJiJJGgtGEwjS3x2v+1Bt8/OUW9JMdiViBYm4oKVnoiMaHR2EWkk6Me3CFgPvElVdxXZ7zqCPwrbVPWQkZzTFJefVSOVzSdVtzZGZEED0aYq3PpY0DrCLlaMqVRly9WmsuVaQ+RaREDPOAbJZJJp06bR0NBAbW1t/oNtIpGwFhCTXCQSoaamhpqamqLbfd/Pt8DIdSFpb2+ntbW1aAEjd8xIJDKVW9BYnjWoenheJ9lsO+CHffcdEvHZ1NUdQjK5kFhsBrFYE66bGO9wjSk7ESESqR714w5WoPi3iLxDVX/cJ5C3EVSLR+Iy4E+qeqWIXBY+/niR/a4Hvgf8uMg2MwzqK9qdDab5DAfhiUxLEF3SEEzx2RDHiVl115gJpJy52lSA3MCUufEJClVXVzNr1iymTZtGTU1NflwIawlhBuI4Tr7FTH19fb/tvu/nixddXV10dnbS2tpKW1sbbW1teJ7Xq3jhum5+0NNJXLywPDvFBC0jOsh6HaBhF2dxSSbm0dCwgkRyPvHYTKLRRhzH8q0xo2mwf1HvA34tIucDDxMMh3gkkATOHuF5zyIYlA3gBuA+ihQoVPV+EVk0wnNNafmCRMoLKr0ORKYnic6tIRqOH2HdNYyZ0MqZq80YU9X8GAO5D4IAiUSC6dOn09jYSF1dXX6QypEM0mhMMY7jDNiVJPf7mStedHR00NLSki9g+L7fa/9c4WISdBuxPDuJ5frZZ7PtYX/6YGrGZGIe9fWHk6xaSDw2i1is0bpoGDMGBixQqOpG4GgROQk4mOD79rtU9U+jcN5Zqro5PM9mEZk50gOKyIXAhQALFy4c6eEmrF5dNhAQiMxIEptbQ6QxgdsQR1wrSBgzWZQ5V5syyjW1T6fT+W+jRYS6ujrmzJlDY2Mj1dXV1NTUlDyQojHlJCL5qWIbGhp6bVNVUqlUfrDO9vZ2du3aRWtrK62trb32y3UXmSitLizPTi65mQs8rwMQFCUem059wxFUVy0mHp9NLNaE40THO1RjpqQ9tklS1XuAe4Z6YBH5I9B/NA24fKjHKoWqXgNcA7BixYop0x9QNSxIdOcKEkqkMUn0gGlEwy4bVpAwZvIbbq42Y6NYMcJxHOrr61m4cGG+i0Z1dbW1ijATUm4K2kSif997z/Py3UXa29tpaWnJt7zwfT/fZcRxnPxUtZVYuLA8O/EUto7wNYMgiBOlumoxVdX7kUzMI5GYHQwCaIypCGXrNKWqrxhom4hsFZE5YeuJOcC2csUx2agqZH38rtwsG+A2xInt00BkRpJIQwKJVt4fdWOMmSpyg1emUql8Nw0RoaGhIV+MqK2tpbq6uiI/hBkz2lzXzQ/cOXNmT6PZ3HgXHR0d+e4iuVYXnufl98tNyTpRWlyY8dMzdkRb0BEHJRprpKFhBdXV+xCPzyEWm46I/R4ZU6nGa1SX24B3AleGt78dpzgmBPXCgkQ26NvpJKPE924gOruaSGMCJ27fthljzHjxPI9UKpUfwFJVqampYeHChTQ1NVFbW0tNTY21jDCmj8LxLpqamvLrVZXu7u584WL37t3s3r2blpaWfIsL3/fzrZHM1KWaJZvt6a6BQDIxn4aGo6mq2otEYi6RSPEZbYwxlWm8ChRXAj8XkXcBLwBvBBCRucAPVPX08PHNBINpNonIBuCzqvrD8Ql57PR02/AARVyH2JxqonNriExP2LSfxhgzTgpbR+QGBIxEIkyfPp0ZM2bQ0NBAXV2djRlhzAiISK/CxV577QUELS5yhYu2tja6u7utRcUU4/tZsl4bvtcJCCIuyarF1NYcQDK5gERiDo4TH+8wjTEjMC4FClVtBk4usn4TcHrB43PGMq7xpFkfvzML4QWvOy1BbL9pRGdUBTNtuFaQMMaYsZYb+C+VSuXXVVdXs9dee9HU1ER9fT1VVVX2IcmYMeA4Tn4q3RkzZox3OGYM9CpIiCASobp6X2pqDiSZWEA8Psum+TRmkrF/0eNEVdHu3GwbIDGX+KJaonNqiExPWrcNY4wZB7k+8alUqtfYEXvvvTfTpk2jvr6eeNy+nTPGmHLo1WUjX5BYQm3NgSSTQUHCpvo0ZnKzAsUY6mklEfSZjExPEDuwkcjMKtzaGOJYKwljjBlLfcePcByH6dOnM3v2bKZNm0ZdXR2RiP2pNMaYclD1yWbb8Lz2cAgJl6rqfaitOZiqqr2sIGHMFGRXXWXUdwpQiTrE96oNxpJoSuLELOEaY8xY6luQcF2XGTNmMGvWrPzsGjaYpTHGlIeq4nmdZL1WUB9EqEruRU3tCVRXLSIen4PjRMc7TGPMOLICxShTX4NWElkPFNxp8WAsiZnhWBLWSsIYY8ZMblC9dDqNiPQqSDQ2NlJbW2vjRxhjTBl5XopstgXVoDAcj8+ioeEkqqv3IZGYj+tatzljTA8rUIwCzfj4XZmg64YjROfUEF9QG7SSSNpbbIwxY6XvoJaO49DU1MTs2bNpbGykrq7OChLGGFNGqlky2TY8rxMB3EgN9Q1HUFuzP8nkQiKR2vEO0RhTwezT8zD07roBEo8Q37eB2JwaIo0JJGIXv8YYMxZUlXQ6TSqVQjUY36exsZElS5bQ2NhIfX29ddkwxpgy6t1tQxFxgoEta5dSVbUXsdgMRKwFsTGmNFagKJH6inZl0YwHgNsQJ7b/NKKzqnHrYpZ4jTFmjGSzWbq6uvB9H1WltrY2P+1nQ0MD0aj1XzbGTAwi8gXgLMAHtgErVXVTn332B35WsGpv4DOq+u2xirMv38+Qzbbg+ykUSMRn0dBwMjU1+5JIzMNxYuMVmjFmgrMCxSDUC2fd8IJBfCIzq4gtrCU2swqnyi6AjTFmLPQdRyIWi7FgwYL8TBuJRGK8QzTGmOH6mqp+GkBELgE+A1xUuIOqPg0sD/dxgY3ArWMZZNBKop1sthUAx4lTU3sQdbUHk0wuIhqtG8twjDGTmBUo+ugZTwLEFWLza4jNr7VZN4wxZozkum10d3cDICLMmDGDuXPnMn36dGpqaqzVmjFmUlDV1oKH1YDu4SknA2tV9fnyRRXw/QyZ7G58P40AicQ8GhtfSnX1viQSc236T2NMWViBIiQCfksaibvE951GbG51MJ6Ea+NJGGPMWEmlUrS2tlJTU8P+++/PjBkzaGhoIBKxP1fGmMlJRL4EvANoAU7cw+5vAW7ew/EuBC4EWLhw4XAiort7I44bp7b2YOrqllKV3MsGtzTGjAm74gvVHDMXpyZqU4EaY8w4mTFjBi9/+ctpbGwkmUyOdzjGGDMqROSPwOwimy5X1d+q6uXA5SLyCeBi4LMDHCcGnAl8YrDzqeo1wDUAK1as2FOLjH7mz3srsdh0ayVhjBkXVqAIxRZYVdgYY8ZTbW0ttbWWi40xk4uqvqLEXX8K3MkABQrgNOARVd06KoEVISLU1y8v1+GNMWaPrP+CMcYYY4wx40BElhQ8PBN4apDdz2EP3TuMMWaiswKFMcYYY4wx4+NKEXlcRB4FXgl8AEBE5orI73I7iUgVcArw6/EJ0xhjxoZ18TDGGGOMMWYcqOrrB1i/CTi94HEnMH2s4jLGmPFiLSiMMcYYY4wxxhgz7qxAYYwxxhhjjDHGmHEnqkOefajiich24PkhPq0J2FGGcIaqEuKohBigMuKohBigMuKohBhgYsexl6rOKEcwlcby8KiohDgqIQaojDgqIQaojDgqIQawPLxHlosnRQxQGXFUQgxQGXFUQgwwseMYMBdPygLFcIjIKlVdYXFURgyVEkclxFApcVRCDBbH5FYp76nFUVkxVEoclRBDpcRRCTFUUhyTTaW8r5UQRyXEUClxVEIMlRJHJcQwmeOwLh7GGGOMMcYYY4wZd1agMMYYY4wxxhhjzLizAkWPa8Y7gFAlxFEJMUBlxFEJMUBlxFEJMYDFMZlVyntqcfSohBigMuKohBigMuKohBigcuKYbCrlfa2EOCohBqiMOCohBqiMOCohBpikcdgYFMYYY4wxxhhjjBl31oLCGGOMMcYYY4wx484KFMYYY4wxxhhjjBl3U6pAISJvFJEnRMQXkQGnQhGRU0XkaRF5VkQuK1jfKCJ3i8h/wttpw4hhj8cQkf1FZHXB0ioiHwy3XSEiGwu2nT7UGIbyWkRkvYg8Fp5r1VCfP9IYRGSBiNwrIk+GP7sPFGwb0Xsx0M+5YLuIyHfD7Y+KyOGlPncUYzg3PPejIvKAiBxasK3oz6ZMcZwgIi0F7/VnSn3uKMbw0YLzPy4inog0httG5b0QketEZJuIPD7A9rL/Tkx2UgF5uNTjSJlzcamvZaDf7zF+L8qSi0v4dz8m/+ZKiKPsubiEGMqeh0uMw3LxJCAVkItLzD12Tdyzj10T2zVxbvvkzsOqOmUW4EBgf+A+YMUA+7jAWmBvIAasAQ4Kt30VuCy8fxnwlWHEMKRjhPFsAfYKH18BXDoK70VJcQDrgaaRvo7hxgDMAQ4P79cCzxT8PIb9Xgz2cy7Y53TgLkCAY4B/lPrcUYzhWGBaeP+0XAyD/WzKFMcJwB3Dee5oxdBn/9cA95ThvTgOOBx4fIDtZf2dmAoLFZCHh3McypCLS41hoN/vsXwvKEMuLjH3lP3fXIlxlDUXlxjDCZQxDw/nWFgunrALFZCLh3oM7JrYrontmrjY/pMuD0+pFhSq+qSqPr2H3Y4CnlXVdaqaBm4Bzgq3nQXcEN6/AXjtMMIY6jFOBtaq6vPDONdoxjHazy/pGKq6WVUfCe+3AU8C84Zxrr4G+zkXxvdjDTwINIjInBKfOyoxqOoDqrorfPggMH8Y5xlxHGV67kiOcw5w8zDOMyhVvR/YOcgu5f6dmPQqJA8P5zjlyMWVkIdLOk6ZcnEl5OGS4hiDXFwJeXg4x7JcPEFVSC6uhDw8nDhG+/klHcOuie2aeACTLg9PqQJFieYBLxY83sD/b+9eY+wo6ziOf3+BltqLNaWhtEEsCa2JRCySqgVNwTSNEG94S42BVoiIEYmYqDE0hoTwRqNvVPqCUps0xhrlIlS01oYKUtRebKlNtQKvSJveVJoabLT9++J5VmZ3z+7O2Z05M/T8PsmTnZ0z88z/zMz57eTpnOlrH/45EXEYUkgAF42j/277WMHwk+7OfCvNuvHcRtZlHQH8WtIuSbePY/0qagBA0nzgKuAPhdnj3RejHeexlimzblU1FN1GGqkcMNKxqauOJZL2SvqlpCu6XLeqGpA0FfgA8HBhdlX7Yix1nxOW1J3D4+mnjixuQw533U+FWdyGHC5bR1EdWdyGHO6qL2dxX/A18WC+Jh6+jK+JfU08oLJz4vwJl9Yykn4DXNzhpXsi4udluugwL6qqoct+JgMfBr5RmL0GuC/XdB/wHeDWGuu4NiIOSboI2CLpL3lErZQK98V00ofvyxFxMs8uvS86ddlh3tDjPNIyEz5HuqghLShdTwrj9xZmT+jYdFnHbtItlaeUvtf4GLCg5LpV1TDgQ8CzEVEc1a1qX4yl7nPinNCGHB6rji77GXcWtyGHK6yj6ixuQw6XrSMtWF8WtyGHy9YxwFnccm3I4jbkcIV1+Jp4+DK+JvY1cVEl58Q5N0AREcsm2MXLwJsLv18CHMrTRyTNjYjD+RaWo93WIKlUH9kNwO6IOFLo+//Tkh4ENo20chV1RMSh/POopEdJt+08TQ/3haRJpCD+UUQ8Uui79L7oYLTjPNYyk0usW1UNSLoSWAvcEBEnBuaPcmwqr6PwB5CIeFLSA5Jml30PVdRQMOxfUCrcF+Ots6pz4pzQhhweq45eZXEbcriqOmrI4jbkcNk66s7iNuRwqToKnMUt14YsbkMOV1WHr4l9TZy34Wvi0euc8Dnhr3gMtwNYIOmyPFq7Ang8v/Y4sDJPrwTKjD4P1U0fw75TlENrwE1AxyerVlGHpGmSZgxMA8sL2+vJvpAk4CHgQER8d8hrE9kXox3nYn23KHkP8Eq+7a7MupXUIOlS4BHg5og4WJg/2rGpo46L87FA0rtI2XGizLpV1ZC3PRNYSuFcqXhfjKXuc8KSunO4237qyuI25HDZOurI4jbkcKk6epDFbcjhUnXk7TuL+4OviV/blq+JfU08sD1fEyf1nRMxwSd8vp4a6QP7MnAaOAJszvPnAU8WlruR9GTcF0m3wQ3MvxDYCvwt/5w1jho69tGhhqmkk33mkPU3APuA5/PBnjvOfTFmHaSnr+7NbX8T+4J0+1bk97sntxur2BedjjNwB3BHnhbwg/z6PgpPuR7pHBnHPhirhrXAPwrvfedYx6amOu7M29lLejDRNb3eF/n3VcDGIetVti9IFz+Hgf+QsuK2Xp8T53qjBTk8Wj8d6qgti8vUMNr53ct9QU1ZPNbnvlefuRJ11J7FJWqoPYfL1JF/X4Wz+HXdaEEWj9RHhxp8TRy+JsbXxH2Tw8qdmJmZmZmZmZk1xl/xMDMzMzMzM7PGeYDCzMzMzMzMzBrnAQozMzMzMzMza5wHKMzMzMzMzMyscR6gMDMzMzMzM7PGeYDCWknSPZL2S3pe0h5J7+5y/fmSxvx/fyXNlbQpT183MN0WkjZKWtB0HWbWn5zFibPYzJriHE6cw/3j/KYLMBtK0hLgg8A7I+K0pNnA5Jo29xXgwZr6rsIa4GvA55ouxMz6i7N4EGexmfWcc3gQ53Cf8B0U1kZzgeMRcRogIo5HxCFJiyVtl7RX0h8lzcijws9I2p3bNUM7k3SepG9L2pFHnz9fePnjwK86rHOvpHWStkl6SdJdhdduyf3slbQhz3uLpK15/lZJl+b56yWtkfRU7mdp7veApPWFPpdLei6/h59Kmp5fegZYJsmDiWbWa85iZ7GZNcs57BzuPxHh5taqBkwH9gAHgQeApaTR4peAxXmZN5LuAJoKTMnzFgA78/R84M95+nZgdZ6+ANgJXJbbrsJ2rwM25el7ge15+dnACWAScAXwV2B2Xm5W/vkEsDJP3wo8lqfXAxsBAR8BTgJvJw0O7gIW5f6fBqbldb4OfLNQ1xbg6qaPi5ubW381Z7Gz2M3NrdnmHHYO92PzCJS1TkScknQ18D7geuAnwP3A4YjYkZc5CSBpGvB9SYuAM8DCDl0uB66U9In8+0xScJ8Cjo1Syi8ijViflnQUmAO8H/hZRBzPdfw9L7sE+Fie3gB8q9DPExERkvYBRyJiX659P+mPxiXA24BnJUH6w/NcYf2jwDxSeJuZ9YSz2FlsZs1yDjuH+5EHKKyVIuIMsA3YlkPsi0B0WPRu4AjwDtII7L87LCPgSxGxedBM6SpgyihlnC5MnyF9XjRCHcPeQod+zg7p82zu8wywJSI+PUJfU4BXS2zTzKxSzuJBnMVm1nPO4UGcw33Az6Cw1pH0Vg1+Su8i4AAwT9LivMyM/B20maRR5LPAzcB5HbrcDHxB0qS87sI8ynyQNFrbja3ApyRdmPualedvB1bk6c8Av+uiz98D10q6PPc5VVJx1HshsL/LOs3MJsRZ7Cw2s2Y5h53D/ch3UFgbTQe+J+lNwH+BF0jfmfthnv8G0ujpMtL38R6W9EngKeBfHfpbSwrd3Ur3ix0DPhoRr0h6UdLlEfFCmcIiYr+k+4HfSjoD/AlYBdwFrJP01dz/Z8u+2Yg4JmkV8GNJF+TZq4GDkuYAr0bE4bL9mZlVxFnsLDazZjmHncN9RxFl7swxOzdJuon0sJ3VTdfSiaS7gZMR8VDTtZiZ1cVZbGbWLOewtYXvoLC+FhGPDtya1lL/JD1gyMzsnOUsNjNrlnPY2sJ3UJiZmZmZmZlZ4/yQTDMzMzMzMzNrnAcozMzMzMzMzKxxHqAwMzMzMzMzs8Z5gMLMzMzMzMzMGucBCjMzMzMzMzNr3P8Apmofmwt+ltUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1296x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Oranje Juice elasticity as a function of income\n",
    "plt.figure(figsize=(18, 10))\n",
    "dic={0:\"Tropicana\", 1:\"Minute.maid\", 2:\"Dominicks\"}\n",
    "for i in range(3):\n",
    "    for j in range(3):\n",
    "        plt.subplot(3, 3, 3 * i + j + 1)\n",
    "        plt.plot(X_test, te_pred[:, i, j],\n",
    "                 color=\"C{}\".format(str(3 * i + j)),\n",
    "                 label=\"OJ Elasticity {} to {}\".format(dic[j], dic[i]))\n",
    "        plt.fill_between(X_test.flatten(),\n",
    "                         te_pred_interval[0][:, i, j],\n",
    "                         te_pred_interval[1][:, i,j],\n",
    "                         color=\"C{}\".format(str(3*i+j)), alpha=.5, label=\"1-99% CI\")\n",
    "        plt.xlabel(r'Scale(Income)')\n",
    "        plt.ylabel('Orange Juice Elasticity')\n",
    "        plt.legend()\n",
    "plt.suptitle(\"Orange Juice Elasticity vs Income\",fontsize=16)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
